thesis paper data mining

82 Data Mining Essay Topic Ideas & Examples

🏆 best data mining topic ideas & essay examples, 💡 good essay topics on data mining, ✅ most interesting data mining topics to write about.

• Disadvantages of Using Web 2.0 for Data Mining Applications This data can be confusing to the readers and may not be reliable. Lastly, with the use of Web 2.
• Data Mining and Its Major Advantages Thus, it is possible to conclude that data mining is a convenient and effective way of processing information, which has many advantages.
• The Data Mining Method in Healthcare and Education Thus, I would use data mining in both cases; however, before that, I would discover a way to improve the algorithms used for it.
• Data Mining Tools and Data Mining Myths The first problem is correlated with keeping the identity of the person evolved in data mining secret. One of the major myths regarding data mining is that it can replace domain knowledge.
• Hybrid Data Mining Approach in Healthcare One of the healthcare projects that will call for the use of data mining is treatment evaluation. In this case, it is essential to realize that the main aim of health data mining is to […]
• Terrorism and Data Mining Algorithms However, this is a necessary evil as the nation’s security has to be prioritized since these attacks lead to harm to a larger population compared to the infringements.
• Transforming Coded and Text Data Before Data Mining However, to complete data mining, it is necessary to transform the data according to the techniques that are to be used in the process.
• Data Mining and Machine Learning Algorithms The shortest distance of string between two instances defines the distance of measure. However, this is also not very clear as to which transformations are summed, and thus it aims to a probability with the […]
• Summary of C4.5 Algorithm: Data Mining 5 algorism: Each record from set of data should be associated with one of the offered classes, it means that one of the attributes of the class should be considered as a class mark.
• Data Mining in Social Networks: Linkedin.com One of the ways to achieve the aim is to understand how users view data mining of their data on LinkedIn.
• Ethnography and Data Mining in Anthropology The study of cultures is of great importance under normal circumstances to enhance the understanding of the same. Data mining is the success secret of ethnography.
• Issues With Data Mining It is necessary to note that the usage of data mining helps FBI to have access to the necessary information for terrorism and crime tracking.
• Large Volume Data Handling: An Efficient Data Mining Solution Data mining is the process of sorting huge amount of data and finding out the relevant data. Data mining is widely used for the maintenance of data which helps a lot to an organization in […]
• Data Mining and Analytical Developments In this era where there is a lot of information to be handled at ago and actually with little available time, it is necessarily useful and wise to analyze data from different viewpoints and summarize […]
• Levi’s Company’s Data Mining & Customer Analytics Levi, the renowned name in jeans is feeling the heat of competition from a number of other brands, which have come upon the scene well after Levi’s but today appear to be approaching Levi’s market […]
• Cryptocurrency Exchange Market Prediction and Analysis Using Data Mining and Artificial Intelligence This paper aims to review the application of A.I.in the context of blockchain finance by examining scholarly articles to determine whether the A.I.algorithm can be used to analyze this financial market.
• “Data Mining and Customer Relationship Marketing in the Banking Industry“ by Chye & Gerry First of all, the article generally elaborates on the notion of customer relationship management, which is defined as “the process of predicting customer behavior and selecting actions to influence that behavior to benefit the company”.
• Data Mining Techniques and Applications The use of data mining to detect disturbances in the ecosystem can help to avert problems that are destructive to the environment and to society.
• Ethical Data Mining in the UAE Traffic Department The research question identified in the assignment two is considered to be the following, namely whether the implementation of the business intelligence into the working process will beneficially influence the work of the Traffic Department […]
• Canadian University Dubai and Data Mining The aim of mining data in the education environment is to enhance the quality of education for the mass through proactive and knowledge-based decision-making approaches.
• Data Mining and Customer Relationship Management As such, CRM not only entails the integration of marketing, sales, customer service, and supply chain capabilities of the firm to attain elevated efficiencies and effectiveness in conveying customer value, but it obliges the organization […]
• E-Commerce: Mining Data for Better Business Intelligence The method allowed the use of Intel and an example to build the study and the literature on data mining for business intelligence to analyze the findings.
• Ethical Implications of Data Mining by Government Institutions Critics of personal data mining insist that it infringes on the rights of an individual and result to the loss of sensitive information.
• Data Mining Role in Companies The increasing adoption of data mining in various sectors illustrates the potential of the technology regarding the analysis of data by entities that seek information crucial to their operations.
• Data Warehouse and Data Mining in Business The circumstances leading to the establishment and development of the concept of data warehousing was attributed to the fact that failure to have a data warehouse led to the need of putting in place large […]
• Data Mining: Concepts and Methods Speed of data mining process is important as it has a role to play in the relevance of the data mined. The accuracy of data is also another factor that can be used to measure […]
• Data Mining Technologies According to Han & Kamber, data mining is the process of discovering correlations, patterns, trends or relationships by searching through a large amount of data that in most circumstances is stored in repositories, business databases […]
• Data Mining: A Critical Discussion In recent times, the relatively new discipline of data mining has been a subject of widely published debate in mainstream forums and academic discourses, not only due to the fact that it forms a critical […]
• Commercial Uses of Data Mining Data mining process entails the use of large relational database to identify the correlation that exists in a given data. The principal role of the applications is to sift the data to identify correlations.
• A Discussion on the Acceptability of Data Mining Today, more than ever before, individuals, organizations and governments have access to seemingly endless amounts of data that has been stored electronically on the World Wide Web and the Internet, and thus it makes much […]
• Applying Data Mining Technology for Insurance Rate Making: Automobile Insurance Example
• Applebee’s, Travelocity and Others: Data Mining for Business Decisions
• Applying Data Mining Procedures to a Customer Relationship
• Business Intelligence as Competitive Tool of Data Mining
• Overview of Accounting Information System Data Mining
• Applying Data Mining Technique to Disassembly Sequence Planning
• Approach for Image Data Mining Cultural Studies
• Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues
• Database Data Mining: The Silent Invasion of Privacy
• Data Management: Data Warehousing and Data Mining
• Constructive Data Mining: Modeling Consumers’ Expenditure in Venezuela
• Data Mining and Its Impact on Healthcare
• Innovations and Perspectives in Data Mining and Knowledge Discovery
• Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
• Linking Data Mining and Anomaly Detection Techniques
• Data Mining and Pattern Recognition Models for Identifying Inherited Diseases
• Credit Card Fraud Detection Through Data Mining
• Data Mining Approach for Direct Marketing of Banking Products
• Constructive Data Mining: Modeling Argentine Broad Money Demand
• Data Mining-Based Dispatching System for Solving the Pickup and Delivery Problem
• Commercially Available Data Mining Tools Used in the Economic Environment
• Data Mining Climate Variability as an Indicator of U.S. Natural Gas
• Analysis of Data Mining in the Pharmaceutical Industry
• Data Mining-Driven Analysis and Decomposition in Agent Supply Chain Management Networks
• Credit Evaluation Model for Banks Using Data Mining
• Data Mining for Business Intelligence: Multiple Linear Regression
• Cluster Analysis for Diabetic Retinopathy Prediction Using Data Mining Techniques
• Data Mining for Fraud Detection Using Invoicing Data
• Jaeger Uses Data Mining to Reduce Losses From Crime and Waste
• Data Mining for Industrial Engineering and Management
• Business Intelligence and Data Mining – Decision Trees
• Data Mining for Traffic Prediction and Intelligent Traffic Management System
• Building Data Mining Applications for CRM
• Data Mining Optimization Algorithms Based on the Swarm Intelligence
• Big Data Mining: Challenges, Technologies, Tools, and Applications
• Data Mining Solutions for the Business Environment
• Overview of Big Data Mining and Business Intelligence Trends
• Data Mining Techniques for Customer Relationship Management
• Classification-Based Data Mining Approach for Quality Control in Wine Production
• Data Mining With Local Model Specification Uncertainty
• Employing Data Mining Techniques in Testing the Effectiveness of Modernization Theory
• Enhancing Information Management Through Data Mining Analytics
• Evaluating Feature Selection Methods for Learning in Data Mining Applications
• Extracting Formations From Long Financial Time Series Using Data Mining
• Financial and Banking Markets and Data Mining Techniques
• Fraudulent Financial Statements and Detection Through Techniques of Data Mining
• Harmful Impact Internet and Data Mining Have on Society
• Informatics, Data Mining, Econometrics, and Financial Economics: A Connection
• Integrating Data Mining Techniques Into Telemedicine Systems
• Investigating Tobacco Usage Habits Using Data Mining Approach
• Electronics Engineering Paper Topics
• Cyber Security Topics
• Google Paper Topics
• Hacking Essay Topics
• Identity Theft Essay Ideas
• Internet Research Ideas
• Microsoft Topics
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2024, March 2). 82 Data Mining Essay Topic Ideas & Examples. https://ivypanda.com/essays/topic/data-mining-essay-topics/

"82 Data Mining Essay Topic Ideas & Examples." IvyPanda , 2 Mar. 2024, ivypanda.com/essays/topic/data-mining-essay-topics/.

IvyPanda . (2024) '82 Data Mining Essay Topic Ideas & Examples'. 2 March.

IvyPanda . 2024. "82 Data Mining Essay Topic Ideas & Examples." March 2, 2024. https://ivypanda.com/essays/topic/data-mining-essay-topics/.

1. IvyPanda . "82 Data Mining Essay Topic Ideas & Examples." March 2, 2024. https://ivypanda.com/essays/topic/data-mining-essay-topics/.

Bibliography

IvyPanda . "82 Data Mining Essay Topic Ideas & Examples." March 2, 2024. https://ivypanda.com/essays/topic/data-mining-essay-topics/.

• Bibliography
• More Referencing guides Blog Automated transliteration Relevant bibliographies by topics
• Automated transliteration
• Relevant bibliographies by topics
• Referencing guides

Trending Data Mining Thesis Topics

            Data mining seems to be the act of analyzing large amounts of data in order to uncover business insights that can assist firms in fixing issues, reducing risks, and embracing new possibilities . This article provides a complete picture on data mining thesis topics where you can get all information regarding data mining research

How does data mining work?

• A standard data mining design begins with the appropriate business statement in the questionnaire, the appropriate data is collected to tackle it, and the data is prepared for the examination.
• What happens in the earlier stages determines how successful the later versions are.
• Data miners should assure the data quality they utilize as input for research because bad data quality results in poor outcomes.
• Establishing a detailed understanding of the design factors, such as the present business scenario, the project’s main business goal, and the performance objectives.
• Identifying the data required to address the problem as well as collecting this from all sorts of sources.
• Addressing any errors and bugs, like incomplete or duplicate data, and processing the data in a suitable format to solve the research questions.
• Algorithms are used to find patterns from data.
• Identifying if or how another model’s output will contribute to the achievement of a business objective.
• In order to acquire the optimum outcome, an iterative process is frequently used to identify the best method.
• Getting the project’s findings suitable for making decisions in real-time

  The techniques and actions listed above are repeated until the best outcomes are achieved. Our engineers and developers have extensive knowledge of the tools, techniques, and approaches used in the processes described above. We guarantee that we will provide the best research advice w.r.t to data mining thesis topics and complete your project on schedule. What are the important data mining tasks?

Data Mining Tasks 

• Data mining finds application in many ways including description, Analysis, summarization of data, and clarifying the conceptual understanding by data description
• And also prediction, classification, dependency analysis, segmentation, and case-based reasoning are some of the important data mining tasks
• Regression – numerical data prediction (stock prices, temperatures, and total sales)
• Data warehousing – business decision making and large-scale data mining
• Classification – accurate prediction of target classes and their categorization
• Association rule learning – market-based analytical tools that were involved in establishing variable data set relationship
• Machine learning – statistical probability-based decision making method without complicated programming
• Data analytics – digital data evaluation for business purposes
• Clustering – dataset partitioning into clusters and subclasses for analyzing natural data structure and format
• Artificial intelligence – human-based Data analytics for reasoning, solving problems, learning, and planning
• Data preparation and cleansing – conversion of raw data into a processed form for identification and removal of errors

You can look at our website for a more in-depth look at all of these operations. We supply you with the needed data, as well as any additional data you may need for your data mining thesis topics . We supply non-plagiarized data mining thesis assistance in any fresh idea of your choice. Let us now discuss the stages in data mining that are to be included in your thesis topics

How to work on a data mining thesis topic? 

 The following are the important stages or phases in developing data mining thesis topics.

• First of all, you need to identify the present demand and address the question
• The next step is defining or specifying the problem
• Collection of data is the third step
• Alternative solutions and designs have to be analyzed in the next step
• The proposed methodology has to be designed
• The system is then to be implemented

Usually, our experts help in writing codes and implementing them successfully without hassles . By consistently following the above steps you can develop one of the best data mining thesis topics of recent days. Furthermore, technically it is important for you to have a better idea of all the tasks and techniques involved in data mining about which we have discussed below

• Data visualization
• Neural networks
• Statistical modeling
• Genetic algorithms and neural networks
• Decision trees and induction
• Discriminant analysis
• Induction techniques
• Association rules and data visualization
• Bayesian networks
• Correlation
• Regression analysis
• Regression analysis and regression trees

If you are looking forward to selecting the best tool for your data mining project then evaluating its consistency and efficiency stands first. For this, you need to gain enough technical data from real-time executed projects for which you can directly contact us. Since we have delivered an ample number of data mining thesis topics successfully we can help you in finding better solutions to all your research issues. What are the points to be remembered about the data mining strategy?

• Furthermore, data mining strategies must be picked before instruments in order to prevent using strategies that do not align with the article’s true purposes.
• The typical data mining strategy has always been to evaluate a variety of methodologies in order to select one which best fits the situation.
• As previously said, there are some principles that may be used to choose effective strategies for data mining projects.
• Since they are easy to handle and comprehend
• They could indeed collaborate with definitional and parametric data
• Tare unaffected by critical values, they could perhaps function with incomplete information
• They could also expose various interrelationships and an absence of linear combinations
• They could indeed handle noise in records
• They can process huge amounts of data.
• Decision trees, on the other hand, have significant drawbacks.
• Many rules are frequently necessary for dependent variables or numerous regressions, and tiny changes in the data can result in very different tree architectures.

All such pros and cons of various data mining aspects are discussed on our website. We will provide you with high-quality research assistance and thesis writing assistance . You may see proof of our skill and the unique approach that we generated in the field by looking at the samples of the thesis that we produced on our website. We also offer an internal review to help you feel more confident. Let us now discuss the recent data mining methodologies

Current methods in Data Mining

• Prediction of data (time series data mining)
• Discriminant and cluster analysis
• Logistic regression and segmentation

Our technical specialists and technicians usually give adequate accurate data, a thorough and detailed explanation, and technical notes for all of these processes and algorithms. As a result, you can get all of your questions answered in one spot. Our technical team is also well-versed in current trends, allowing us to provide realistic explanations for all new developments. We will now talk about the latest data mining trends

Latest Trending Data Mining Thesis Topics

• Visual data mining and data mining software engineering
• Interaction and scalability in data mining
• Exploring applications of data mining
• Biological and visual data mining
• Cloud computing and big data integration
• Data security and protecting privacy in data mining
• Novel methodologies in complex data mining
• Data mining in multiple databases and rationalities
• Query language standardization in data mining
• Integration of MapReduce, Amazon EC2, S3, Apache Spark, and Hadoop into data mining

These are the recent trends in data mining. We insist that you choose one of the topics that interest you the most. Having an appropriate content structure or template is essential while writing a thesis . We design the plan in a chronological order relevant to the study assessment with this in mind. The incorporation of citations is one of the most important aspects of the thesis. We focus not only on authoring but also on citing essential sources in the text. Students frequently struggle to deal with appropriate proposals when commencing their thesis. We have years of experience in providing the greatest study and data mining thesis writing services to the scientific community, which are promptly and widely acknowledged. We will now talk about future research directions of research in various data mining thesis topics

Future Research Directions of Data Mining

• The potential of data mining and data science seems promising, as the volume of data continues to grow.
• It is expected that the total amount of data in our digital cosmos will have grown from 4.4 zettabytes to 44 zettabytes.
• We’ll also generate 1.7 gigabytes of new data for every human being on this planet each second.
• Mining algorithms have completely transformed as technology has advanced, and thus have tools for obtaining useful insights from data.
• Only corporations like NASA could utilize their powerful computers to examine data once upon a time because the cost of producing and processing data was simply too high.
• Organizations are now using cloud-based data warehouses to accomplish any kinds of great activities with machine learning, artificial intelligence, and deep learning.

The Internet of Things as well as wearable electronics, for instance, has transformed devices to be connected into data-generating engines which provide limitless perspectives into people and organizations if firms can gather, store, and analyze the data quickly enough. What are the aspects to be remembered for choosing the best  data mining thesis topics?

• An excellent thesis topic is a broad concept that has to be developed, verified, or refuted.
• Your thesis topic must capture your curiosity, as well as the involvement of both the supervisor and the academicians.
• Your thesis topic must be relevant to your studies and should be able to withstand examination.

Our engineers and experts can provide you with any type of research assistance on any of these data mining development tools . We satisfy the criteria of your universities by ensuring several revisions, appropriate formatting and editing of your thesis, comprehensive grammar check, and so on . As a result, you can contact us with confidence for complete assistance with your data mining thesis. What are the important data mining thesis topics?

Research Topics in Data Mining

• Handling cost-effective, unbalanced non-static data
• Issues related to data mining and their solutions
• Network settings in data mining and ensuring privacy, security, and integrity of data
• Environmental and biological issues in data mining
• Complex data mining and sequential data mining (time series data)
• Data mining at higher dimensions
• Multi-agent data mining and distributed data mining
• High-speed data mining
• Development of unified data mining theory

We currently provide full support for all parts of research study, development, investigation, including project planning, technical advice, legitimate scientific data, thesis writing, paper publication, assignments and project planning, internal review, and many other services. As a result, you can contact us for any kind of help with your data mining thesis topics.

Why Work With Us ?

Senior research member, research experience, journal member, book publisher, research ethics, business ethics, valid references, explanations, paper publication, 9 big reasons to select us.

Our Editor-in-Chief has Website Ownership who control and deliver all aspects of PhD Direction to scholars and students and also keep the look to fully manage all our clients.

Our world-class certified experts have 18+years of experience in Research & Development programs (Industrial Research) who absolutely immersed as many scholars as possible in developing strong PhD research projects.

We associated with 200+reputed SCI and SCOPUS indexed journals (SJR ranking) for getting research work to be published in standard journals (Your first-choice journal).

PhDdirection.com is world’s largest book publishing platform that predominantly work subject-wise categories for scholars/students to assist their books writing and takes out into the University Library.

Our researchers provide required research ethics such as Confidentiality & Privacy, Novelty (valuable research), Plagiarism-Free, and Timely Delivery. Our customers have freedom to examine their current specific research activities.

Our organization take into consideration of customer satisfaction, online, offline support and professional works deliver since these are the actual inspiring business factors.

Solid works delivering by young qualified global research team. "References" is the key to evaluating works easier because we carefully assess scholars findings.

Detailed Videos, Readme files, Screenshots are provided for all research projects. We provide Teamviewer support and other online channels for project explanation.

Worthy journal publication is our main thing like IEEE, ACM, Springer, IET, Elsevier, etc. We substantially reduces scholars burden in publication side. We carry scholars from initial submission to final acceptance.

Related Pages

Our benefits, throughout reference, confidential agreement, research no way resale, plagiarism-free, publication guarantee, customize support, fair revisions, business professionalism, domains & tools, we generally use, wireless communication (4g lte, and 5g), ad hoc networks (vanet, manet, etc.), wireless sensor networks, software defined networks, network security, internet of things (mqtt, coap), internet of vehicles, cloud computing, fog computing, edge computing, mobile computing, mobile cloud computing, ubiquitous computing, digital image processing, medical image processing, pattern analysis and machine intelligence, geoscience and remote sensing, big data analytics, data mining, power electronics, web of things, digital forensics, natural language processing, automation systems, artificial intelligence, mininet 2.1.0, matlab (r2018b/r2019a), matlab and simulink, apache hadoop, apache spark mlib, apache mahout, apache flink, apache storm, apache cassandra, pig and hive, rapid miner, support 24/7, call us @ any time, +91 9444829042, [email protected].

Questions ?

Click here to chat with us

M.Tech/Ph.D Thesis Help in Chandigarh | Thesis Guidance in Chandigarh

[email protected]

+91-9465330425

Data Mining

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• PeerJ Comput Sci

Adaptations of data mining methodologies: a systematic literature review

Associated data.

The following information was supplied regarding data availability:

SLR Protocol (also shared via online repository), corpus with definitions and mappings are provided as a Supplemental File .

The use of end-to-end data mining methodologies such as CRISP-DM, KDD process, and SEMMA has grown substantially over the past decade. However, little is known as to how these methodologies are used in practice. In particular, the question of whether data mining methodologies are used ‘as-is’ or adapted for specific purposes, has not been thoroughly investigated. This article addresses this gap via a systematic literature review focused on the context in which data mining methodologies are used and the adaptations they undergo. The literature review covers 207 peer-reviewed and ‘grey’ publications. We find that data mining methodologies are primarily applied ‘as-is’. At the same time, we also identify various adaptations of data mining methodologies and we note that their number is growing rapidly. The dominant adaptations pattern is related to methodology adjustments at a granular level (modifications) followed by extensions of existing methodologies with additional elements. Further, we identify two recurrent purposes for adaptation: (1) adaptations to handle Big Data technologies, tools and environments (technological adaptations); and (2) adaptations for context-awareness and for integrating data mining solutions into business processes and IT systems (organizational adaptations). The study suggests that standard data mining methodologies do not pay sufficient attention to deployment issues, which play a prominent role when turning data mining models into software products that are integrated into the IT architectures and business processes of organizations. We conclude that refinements of existing methodologies aimed at combining data, technological, and organizational aspects, could help to mitigate these gaps.

Introduction

The availability of Big Data has stimulated widespread adoption of data mining and data analytics in research and in business settings ( Columbus, 2017 ). Over the years, a certain number of data mining methodologies have been proposed, and these are being used extensively in practice and in research. However, little is known about what and how data mining methodologies are applied, and it has not been neither widely researched nor discussed. Further, there is no consolidated view on what constitutes quality of methodological process in data mining and data analytics, how data mining and data analytics are applied/used in organization settings context, and how application practices relate to each other. That motivates the need for comprehensive survey in the field.

There have been surveys or quasi-surveys and summaries conducted in related fields. Notably, there have been two systematic systematic literature reviews; Systematic Literature Review, hereinafter, SLR is the most suitable and widely used research method for identifying, evaluating and interpreting research of particular research question, topic or phenomenon ( Kitchenham, Budgen & Brereton, 2015 ). These reviews concerned Big Data Analytics, but not general purpose data mining methodologies. Adrian et al. (2004) executed SLR with respect to implementation of Big Data Analytics (BDA), specifically, capability components necessary for BDA value discovery and realization. The authors identified BDA implementation studies, determined their main focus areas, and discussed in detail BDA applications and capability components. Saltz & Shamshurin (2016) have published SLR paper on Big Data Team Process Methodologies. Authors have identified lack of standard in regards to how Big Data projects are executed, highlighted growing research in this area and potential benefits of such process standard. Additionally, authors synthesized and produced list of 33 most important success factors for executing Big Data activities. Finally, there are studies that surveyed data mining techniques and applications across domains, yet, they focus on data mining process artifacts and outcomes ( Madni, Anwar & Shah, 2017 ; Liao, Chu & Hsiao, 2012 ), but not on end-to-end process methodology.

There have been number of surveys conducted in domain-specific settings such as hospitality, accounting, education, manufacturing, and banking fields. Mariani et al. (2018) focused on Business Intelligence (BI) and Big Data SLR in the hospitality and tourism environment context. Amani & Fadlalla (2017) explored application of data mining methods in accounting while Romero & Ventura (2013) investigated educational data mining. Similarly, Hassani, Huang & Silva (2018) addressed data mining application case studies in banking and explored them by three dimensions—topics, applied techniques and software. All studies were performed by the means of systematic literature reviews. Lastly, Bi & Cochran (2014) have undertaken standard literature review of Big Data Analytics and its applications in manufacturing.

Apart from domain-specific studies, there have been very few general purpose surveys with comprehensive overview of existing data mining methodologies, classifying and contextualizing them. Valuable synthesis was presented by Kurgan & Musilek (2006) as comparative study of the state-of-the art of data mining methodologies. The study was not SLR, and focused on comprehensive comparison of phases, processes, activities of data mining methodologies; application aspect was summarized briefly as application statistics by industries and citations. Three more comparative, non-SLR studies were undertaken by Marban, Mariscal & Segovia (2009) , Mariscal, Marbán & Fernández (2010) , and the most recent and closest one by Martnez-Plumed et al. (2017) . They followed the same pattern with systematization of existing data mining frameworks based on comparative analysis. There, the purpose and context of consolidation was even more practical—to support derivation and proposal of the new artifact, that is, novel data mining methodology. The majority of the given general type surveys in the field are more than a decade old, and have natural limitations due to being: (1) non-SLR studies, and (2) so far restricted to comparing methodologies in terms of phases, activities, and other elements.

The key common characteristic behind all the given studies is that data mining methodologies are treated as normative and standardized (‘one-size-fits-all’) processes. A complementary perspective, not considered in the above studies, is that data mining methodologies are not normative standardized processes, but instead, they are frameworks that need to be specialized to different industry domains, organizational contexts, and business objectives. In the last few years, a number of extensions and adaptations of data mining methodologies have emerged, which suggest that existing methodologies are not sufficient to cover the needs of all application domains. In particular, extensions of data mining methodologies have been proposed in the medical domain ( Niaksu, 2015 ), educational domain ( Tavares, Vieira & Pedro, 2017 ), the industrial engineering domain ( Huber et al., 2019 ; Solarte, 2002 ), and software engineering ( Marbán et al., 2007 , 2009 ). However, little attention has been given to studying how data mining methodologies are applied and used in industry settings, so far only non-scientific practitioners’ surveys provide such evidence.

Given this research gap, the central objective of this article is to investigate how data mining methodologies are applied by researchers and practitioners, both in their generic (standardized) form and in specialized settings. This is achieved by investigating if data mining methodologies are applied ‘as-is’ or adapted, and for what purposes such adaptations are implemented.

Guided by Systematic Literature Review method, initially we identified a corpus of primary studies covering both peer-reviewed and ‘grey’ literature from 1997 to 2018. An analysis of these studies led us to a taxonomy of uses of data mining methodologies, focusing on the distinction between ‘as is’ usage versus various types of methodology adaptations. By analyzing different types of methodology adaptations, this article identifies potential gaps in standard data mining methodologies both at the technological and at the organizational levels.

The rest of the article is organized as follows. The Background section provides an overview of key concepts of data mining and associated methodologies. Next, Research Design describes the research methodology. The Findings and Discussion section presents the study results and their associated interpretation. Finally, threats to validity are addressed in Threats to Validity while the Conclusion summarizes the findings and outlines directions for future work.

The section introduces main data mining concepts, provides overview of existing data mining methodologies, and their evolution.

Data mining is defined as a set of rules, processes, algorithms that are designed to generate actionable insights, extract patterns, and identify relationships from large datasets ( Morabito, 2016 ). Data mining incorporates automated data extraction, processing, and modeling by means of a range of methods and techniques. In contrast, data analytics refers to techniques used to analyze and acquire intelligence from data (including ‘big data’) ( Gandomi & Haider, 2015 ) and is positioned as a broader field, encompassing a wider spectrum of methods that includes both statistical and data mining ( Chen, Chiang & Storey, 2012 ). A number of algorithms has been developed in statistics, machine learning, and artificial intelligence domains to support and enable data mining. While statistical approaches precedes them, they inherently come with limitations, the most known being rigid data distribution conditions. Machine learning techniques gained popularity as they impose less restrictions while deriving understandable patterns from data ( Bose & Mahapatra, 2001 ).

Data mining projects commonly follow a structured process or methodology as exemplified by Mariscal, Marbán & Fernández (2010) , Marban, Mariscal & Segovia (2009) . A data mining methodology specifies tasks, inputs, outputs, and provides guidelines and instructions on how the tasks are to be executed ( Mariscal, Marbán & Fernández, 2010 ). Thus, data mining methodology provides a set of guidelines for executing a set of tasks to achieve the objectives of a data mining project ( Mariscal, Marbán & Fernández, 2010 ).

The foundations of structured data mining methodologies were first proposed by Fayyad, Piatetsky-Shapiro & Smyth (1996a , 1996b , 1996c) , and were initially related to Knowledge Discovery in Databases (KDD). KDD presents a conceptual process model of computational theories and tools that support information extraction (knowledge) with data ( Fayyad, Piatetsky-Shapiro & Smyth, 1996a ). In KDD, the overall approach to knowledge discovery includes data mining as a specific step. As such, KDD, with its nine main steps (exhibited in Fig. 1 ), has the advantage of considering data storage and access, algorithm scaling, interpretation and visualization of results, and human computer interaction ( Fayyad, Piatetsky-Shapiro & Smyth, 1996a , 1996c ). Introduction of KDD also formalized clearer distinction between data mining and data analytics, as for example formulated in Tsai et al. (2015) : “…by the data analytics, we mean the whole KDD process, while by the data analysis, we mean the part of data analytics that is aimed at finding the hidden information in the data, such as data mining”.

The main steps of KDD are as follows:

• Step 1: Learning application domain: In the first step, it is needed to develop an understanding of the application domain and relevant prior knowledge followed by identifying the goal of the KDD process from the customer’s viewpoint.
• Step 2: Dataset creation: Second step involves selecting a dataset, focusing on a subset of variables or data samples on which discovery is to be performed.
• Step 3: Data cleaning and processing: In the third step, basic operations to remove noise or outliers are performed. Collection of necessary information to model or account for noise, deciding on strategies for handling missing data fields, and accounting for data types, schema, and mapping of missing and unknown values are also considered.
• Step 4: Data reduction and projection: Here, the work of finding useful features to represent the data, depending on the goal of the task, application of transformation methods to find optimal features set for the data is conducted.
• Step 5: Choosing the function of data mining: In the fifth step, the target outcome (e.g., summarization, classification, regression, clustering) are defined.
• Step 6: Choosing data mining algorithm: Sixth step concerns selecting method(s) to search for patterns in the data, deciding which models and parameters are appropriate and matching a particular data mining method with the overall criteria of the KDD process.
• Step 7: Data mining: In the seventh step, the work of mining the data that is, searching for patterns of interest in a particular representational form or a set of such representations: classification rules or trees, regression, clustering is conducted.
• Step 8: Interpretation: In this step, the redundant and irrelevant patterns are filtered out, relevant patterns are interpreted and visualized in such way as to make the result understandable to the users.
• Step 9: Using discovered knowledge: In the last step, the results are incorporated with the performance system, documented and reported to stakeholders, and used as basis for decisions.

The KDD process became dominant in industrial and academic domains ( Kurgan & Musilek, 2006 ; Marban, Mariscal & Segovia, 2009 ). Also, as timeline-based evolution of data mining methodologies and process models shows ( Fig. 2 below), the original KDD data mining model served as basis for other methodologies and process models, which addressed various gaps and deficiencies of original KDD process. These approaches extended the initial KDD framework, yet, extension degree has varied ranging from process restructuring to complete change in focus. For example, Brachman & Anand (1996) and further Gertosio & Dussauchoy (2004) (in a form of case study) introduced practical adjustments to the process based on iterative nature of process as well as interactivity. The complete KDD process in their view was enhanced with supplementary tasks and the focus was changed to user’s point of view (human-centered approach), highlighting decisions that need to be made by the user in the course of data mining process. In contrast, Cabena et al. (1997) proposed different number of steps emphasizing and detailing data processing and discovery tasks. Similarly, in a series of works Anand & Büchner (1998) , Anand et al. (1998) , Buchner et al. (1999) presented additional data mining process steps by concentrating on adaptation of data mining process to practical settings. They focused on cross-sales (entire life-cycles of online customer), with further incorporation of internet data discovery process (web-based mining). Further, Two Crows data mining process model is consultancy originated framework that has defined the steps differently, but is still close to original KDD. Finally, SEMMA (Sample, Explore, Modify, Model and Assess) based on KDD, was developed by SAS institute in 2005 ( SAS Institute Inc., 2017 ). It is defined as a logical organization of the functional toolset of SAS Enterprise Miner for carrying out the core tasks of data mining. Compared to KDD, this is vendor-specific process model which limits its application in different environments. Also, it skips two steps of original KDD process (‘Learning Application Domain’ and ‘Using of Discovered Knowledge’) which are regarded as essential for success of data mining project ( Mariscal, Marbán & Fernández, 2010 ). In terms of adoption, new KDD-based proposals received limited attention across academia and industry ( Kurgan & Musilek, 2006 ; Marban, Mariscal & Segovia, 2009 ). Subsequently, most of these methodologies converged into the CRISP-DM methodology.

Additionally, there have only been two non-KDD based approaches proposed alongside extensions to KDD. The first one is 5A’s approach presented by De Pisón Ascacbar (2003) and used by SPSS vendor. The key contribution of this approach has been related to adding ‘Automate’ step while disadvantage was associated with omitting ‘Data Understanding’ step. The second approach was 6-Sigma which is industry originated method to improve quality and customer’s satisfaction ( Pyzdek & Keller, 2003 ). It has been successfully applied to data mining projects in conjunction with DMAIC performance improvement model (Define, Measure, Analyze, Improve, Control).

In 2000, as response to common issues and needs ( Marban, Mariscal & Segovia, 2009 ), an industry-driven methodology called Cross-Industry Standard Process for Data Mining (CRISP-DM) was introduced as an alternative to KDD. It also consolidated original KDD model and its various extensions. While CRISP-DM builds upon KDD, it consists of six phases that are executed in iterations ( Marban, Mariscal & Segovia, 2009 ). The iterative executions of CRISP-DM stand as the most distinguishing feature compared to initial KDD that assumes a sequential execution of its steps. CRISP-DM, much like KDD, aims at providing practitioners with guidelines to perform data mining on large datasets. However,CRISP-DM with its six main steps with a total of 24 tasks and outputs, is more refined as compared to KDD. The main steps of CRIPS-DM, as depicted in Fig. 3 below are as follows:

• Phase 1: Business understanding: The focus of the first step is to gain an understanding of the project objectives and requirements from a business perspective followed by converting these into data mining problem definitions. Presentation of a preliminary plan to achieve the objectives are also included in this first step.
• Phase 2: Data understanding: This step begins with an initial data collection and proceeds with activities in order to get familiar with the data, identify data quality issues, discover first insights into the data, and potentially detect and form hypotheses.
• Phase 3: Data preparation: The third step covers activities required to construct the final dataset from the initial raw data. Data preparation tasks are performed repeatedly.
• Phase 4: Modeling phase: In this step, various modeling techniques are selected and applied followed by calibrating their parameters. Typically, several techniques are used for the same data mining problem.
• Phase 5: Evaluation of the model(s): The fifth step begins with the quality perspective and then, before proceeding to final model deployment, ascertains that the model(s) achieves the business objectives. At the end of this phase, a decision should be reached on how to use data mining results.
• Phase 6: Deployment phase: In the final step, the models are deployed to enable end-customers to use the data as basis for decisions, or support in the business process. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized, presented, distributed in a way that the end-user can use it. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data mining process.

The development of CRISP-DM was led by industry consortium. It is designed to be domain-agnostic ( Mariscal, Marbán & Fernández, 2010 ) and as such, is now widely used by industry and research communities ( Marban, Mariscal & Segovia, 2009) . These distinctive characteristics have made CRISP-DM to be considered as ‘de-facto’ standard of data mining methodology and as a reference framework to which other methodologies are benchmarked ( Mariscal, Marbán & Fernández, 2010 ).

Similarly to KDD, a number of refinements and extensions of the CRISP-DM methodology have been proposed with the two main directions—extensions of the process model itself and adaptations, merger with the process models and methodologies in other domains. Extensions direction of process models could be exemplified by Cios & Kurgan (2005) who have proposed integrated Data Mining & Knowledge Discovery (DMKD) process model. It contains several explicit feedback mechanisms, modification of the last step to incorporate discovered knowledge and insights application as well as relies on technologies for results deployment. In the same vein, Moyle & Jorge (2001) , Blockeel & Moyle (2002) proposed Rapid Collaborative Data Mining System (RAMSYS) framework—this is both data mining methodology and system for remote collaborative data mining projects. The RAMSYS attempted to achieve the combination of a problem solving methodology, knowledge sharing, and ease of communication. It intended to allow the collaborative work of remotely placed data miners in a disciplined manner as regards information flow while allowing the free flow of ideas for problem solving ( Moyle & Jorge, 2001 ). CRISP-DM modifications and integrations with other specific domains were proposed in Industrial Engineering (Data Mining for Industrial Engineering by Solarte (2002) ), and Software Engineering by Marbán et al. (2007 , 2009) . Both approaches enhanced CRISP-DM and contributed with additional phases, activities and tasks typical for engineering processes, addressing on-going support ( Solarte, 2002 ), as well as project management, organizational and quality assurance tasks ( Marbán et al., 2009 ).

Finally, limited number of attempts to create independent or semi-dependent data mining frameworks was undertaken after CRISP-DM creation. These efforts were driven by industry players and comprised KDD Roadmap by Debuse et al. (2001) for proprietary predictive toolkit (Lanner Group), and recent effort by IBM with Analytics Solutions Unified Method for Data Mining (ASUM-DM) in 2015 ( IBM Corporation, 2016 : https://developer.ibm.com/technologies/artificial-intelligence/articles/architectural-thinking-in-the-wild-west-of-data-science/ ). Both frameworks contributed with additional tasks, for example, resourcing in KDD Roadmap, or hybrid approach assumed in ASUM, for example, combination of agile and traditional implementation principles.

The Table 1 above summarizes reviewed data mining process models and methodologies by their origin, basis and key concepts.

Research Design

The main research objective of this article is to study how data mining methodologies are applied by researchers and practitioners. To this end, we use systematic literature review (SLR) as scientific method for two reasons. Firstly, systematic review is based on trustworthy, rigorous, and auditable methodology. Secondly, SLR supports structured synthesis of existing evidence, identification of research gaps, and provides framework to position new research activities ( Kitchenham, Budgen & Brereton, 2015 ). For our SLR, we followed the guidelines proposed by Kitchenham, Budgen & Brereton (2015) . All SLR details have been documented in the separate, peer-reviewed SLR protocol (available at https://figshare.com/articles/Systematic-Literature-Review-Protocol/10315961 ).

Research questions

As suggested by Kitchenham, Budgen & Brereton (2015) , we have formulated research questions and motivate them as follows. In the preliminary phase of research we have discovered very limited number of studies investigating data mining methodologies application practices as such. Further, we have discovered number of surveys conducted in domain-specific settings, and very few general purpose surveys, but none of them considered application practices either. As contrasting trend, recent emergence of limited number of adaptation studies have clearly pinpointed the research gap existing in the area of application practices. Given this research gap, in-depth investigation of this phenomenon led us to ask: “How data mining methodologies are applied (‘as-is’ vs adapted) (RQ1)?” Further, as we intended to investigate in depth universe of adaptations scenarios, this naturally led us to RQ2: “How have existing data mining methodologies been adapted?” Finally, if adaptions are made, we wish to explore what the associated reasons and purposes are, which in turn led us to RQ3: “For what purposes are data mining methodologies adapted?”

Thus, for this review, there are three research questions defined:

• Research Question 1: How data mining methodologies are applied (‘as-is’ versus adapted)? This question aims to identify data mining methodologies application and usage patterns and trends.
• Research Question 2: How have existing data mining methodologies been adapted? This questions aims to identify and classify data mining methodologies adaptation patterns and scenarios.
• Research Question 3: For what purposes have existing data mining methodologies been adapted? This question aims to identify, explain, classify and produce insights on what are the reasons and what benefits are achieved by adaptations of existing data mining methodologies. Specifically, what gaps do these adaptations seek to fill and what have been the benefits of these adaptations. Such systematic evidence and insights will be valuable input to potentially new, refined data mining methodology. Insights will be of interest to practitioners and researchers.

Data collection strategy

Our data collection and search strategy followed the guidelines proposed by Kitchenham, Budgen & Brereton (2015) . It defined the scope of the search, selection of literature and electronic databases, search terms and strings as well as screening procedures.

Primary search

The primary search aimed to identify an initial set of papers. To this end, the search strings were derived from the research objective and research questions. The term ‘data mining’ was the key term, but we also included ‘data analytics’ to be consistent with observed research practices. The terms ‘methodology’ and ‘framework’ were also included. Thus, the following search strings were developed and validated in accordance with the guidelines suggested by Kitchenham, Budgen & Brereton (2015) :

(‘data mining methodology’) OR (‘data mining framework’) OR (‘data analytics methodology’) OR (‘data analytics framework’)

The search strings were applied to the indexed scientific databases Scopus, Web of Science (for ‘peer-reviewed’, academic literature) and to the non-indexed Google Scholar (for non-peer-reviewed, so-called ‘grey’ literature). The decision to cover ‘grey’ literature in this research was motivated as follows. As proposed in number of information systems and software engineering domain publications ( Garousi, Felderer & Mäntylä, 2019 ; Neto et al., 2019 ), SLR as stand-alone method may not provide sufficient insight into ‘state of practice’. It was also identified ( Garousi, Felderer & Mäntylä, 2016 ) that ‘grey’ literature can give substantial benefits in certain areas of software engineering, in particular, when the topic of research is related to industrial and practical settings. Taking into consideration the research objectives, which is investigating data mining methodologies application practices, we have opted for inclusion of elements of Multivocal Literature Review (MLR) 1 in our study. Also, Kitchenham, Budgen & Brereton (2015) recommends including ‘grey’ literature to minimize publication bias as positive results and research outcomes are more likely to be published than negative ones. Following MLR practices, we also designed inclusion criteria for types of ‘grey’ literature reported below.

The selection of databases is motivated as follows. In case of peer-reviewed literature sources we concentrated to avoid potential omission bias. The latter is discussed in IS research ( Levy & Ellis, 2006 ) in case research is concentrated in limited disciplinary data sources. Thus, broad selection of data sources including multidisciplinary-oriented (Scopus, Web of Science, Wiley Online Library) and domain-oriented (ACM Digital Library, IEEE Xplorer Digital Library) scientific electronic databases was evaluated. Multidisciplinary databases have been selected due to wider domain coverage and it was validated and confirmed that they do include publications originating from domain-oriented databases, such as ACM and IEEE. From multi-disciplinary databases as such, Scopus was selected due to widest possible coverage (it is worlds largest database, covering app. 80% of all international peer-reviewed journals) while Web of Science was selected due to its longer temporal range. Thus, both databases complement each other. The selected non-indexed database source for ‘grey’ literature is Google Scholar, as it is comprehensive source of both academic and ‘grey’ literature publications and referred as such extensively ( Garousi, Felderer & Mäntylä, 2019 ; Neto et al., 2019 ).

Further, Garousi, Felderer & Mäntylä (2019) presented three-tier categorization framework for types of ‘grey literature’. In our study we restricted ourselves to the 1st tier ‘grey’ literature publications of the limited number of ‘grey’ literature producers. In particular, from the list of producers ( Neto et al., 2019 ) we have adopted and focused on government departments and agencies, non-profit economic, trade organizations (‘think-tanks’) and professional associations, academic and research institutions, businesses and corporations (consultancy companies and established private companies). The 1st tier ‘grey’ literature selected items include: (1) government, academic, and private sector consultancy reports 2 , (2) theses (not lower than Master level) and PhD Dissertations, (3) research reports, (4) working papers, (5) conference proceedings, preprints. With inclusion of the 1st tier ‘grey’ literature criteria we mitigate quality assessment challenge especially relevant and reported for it ( Garousi, Felderer & Mäntylä, 2019 ; Neto et al., 2019 ).

Scope and domains inclusion

As recommended by Kitchenham, Budgen & Brereton (2015) it is necessary to initially define research scope. To clarify the scope, we defined what is not included and is out of scope of this research. The following aspects are not included in the scope of our study:

• Context of technology and infrastructure for data mining/data analytics tasks and projects.
• Granular methods application in data mining process itself or their application for data mining tasks, for example, constructing business queries or applying regression or neural networks modeling techniques to solve classification problems. Studies with granular methods are included in primary texts corpus as long as method application is part of overall methodological approach.
• Technological aspects in data mining for example, data engineering, dataflows and workflows.
• Traditional statistical methods not associated with data mining directly including statistical control methods.

Similarly to Budgen et al. (2006) and Levy & Ellis (2006) , initial piloting revealed that search engines retrieved literature available for all major scientific domains including ones outside authors’ area of expertise (e.g., medicine). Even though such studies could be retrieved, it would be impossible for us to analyze and correctly interpret literature published outside the possessed area of expertise. The adjustments toward search strategy were undertaken by retaining domains closely associated with Information Systems, Software Engineering research. Thus, for Scopus database the final set of inclusive domains was limited to nine and included Computer Science, Engineering, Mathematics, Business, Management and Accounting, Decision Science, Economics, Econometrics and Finance, and Multidisciplinary as well as Undefined studies. Excluded domains covered 11.5% or 106 out of 925 publications; it was confirmed in validation process that they primarily focused on specific case studies in fundamental sciences and medicine 3 . The included domains from Scopus database were mapped to Web of Science to ensure consistent approach across databases and the correctness of mapping was validated.

Screening criteria and procedures

Based on the SLR practices (as in Kitchenham, Budgen & Brereton (2015) , Brereton et al. (2007) ) and defined SLR scope, we designed multi-step screening procedures (quality and relevancy) with associated set of Screening Criteria and Scoring System . The purpose of relevancy screening is to find relevant primary studies in an unbiased way ( Vanwersch et al., 2011 ). Quality screening, on the other hand, aims to assess primary relevant studies in terms of quality in unbiased way.

Screening Criteria consisted of two subsets— Exclusion Criteria applied for initial filtering and Relevance Criteria , also known as Inclusion Criteria .

Exclusion Criteria were initial threshold quality controls aiming at eliminating studies with limited or no scientific contribution. The exclusion criteria also address issues of understandability, accessability and availability. The Exclusion Criteria were as follows:

• Quality 1: The publication item is not in English (understandability).
• either the same document retrieved from two or all three databases.
• or different versions of the same publication are retrieved (i.e., the same study published in different sources)—based on best practices, decision rule is that the most recent paper is retained as well as the one with the highest score ( Kofod-Petersen, 2014 ).
• if a publication is published both as conference proceeding and as journal article with the same name and same authors or as an extended version of conference paper, the latter is selected.
• Quality 3: Length of the publication is less than 6 pages—short papers do not have the space to expand and discuss presented ideas in sufficient depth to examine for us.
• Quality 4: The paper is not accessible in full length online through the university subscription of databases and via Google Scholar—not full availability prevents us from assessing and analyzing the text.

The initially retrieved list of papers was filtered based on Exclusion Criteria . Only papers that passed all criteria were retained in the final studies corpus. Mapping of criteria towards screening steps is exhibited in Fig. 4 .

Relevance Criteria were designed to identify relevant publications and are presented in Table 2 below while mapping to respective process steps is presented in Fig. 4 . These criteria were applied iteratively.

As a final SLR step, the full texts quality assessment was performed with constructed Scoring Metrics (in line with Kitchenham & Charters (2007) ). It is presented in the Table 3 below.

Data extraction and screening process

The conducted data extraction and screening process is presented in Fig. 4 . In Step 1 initial publications list were retrieved from pre-defined databases—Scopus, Web of Science, Google Scholar. The lists were merged and duplicates eliminated in Step 2. Afterwards, texts being less than 6 pages were excluded (Step 3). Steps 1–3 were guided by Exclusion Criteria . In the next stage (Step 4), publications were screened by Title based on pre-defined Relevance Criteria . The ones which passed were evaluated by their availability (Step 5). As long as study was available, it was evaluated again by the same pre-defined Relevance Criteria applied to Abstract, Conclusion and if necessary Introduction (Step 6). The ones which passed this threshold formed primary publications corpus extracted from databases in full. These primary texts were evaluated again based on full text (Step 7) applying Relevance Criteria first and then Scoring Metrics .

Results and quantitative analysis

In Step 1, 1,715 publications were extracted from relevant databases with the following composition—Scopus (819), Web of Science (489), Google Scholar (407). In terms of scientific publication domains, Computer Science (42.4%), Engineering (20.6%), Mathematics (11.1%) accounted for app. 74% of Scopus originated texts. The same applies to Web of Science harvest. Exclusion Criteria application produced the following results. In Step 2, after eliminating duplicates, 1,186 texts were passed for minimum length evaluation, and 767 reached assessment by Relevancy Criteria .

As mentioned Relevance Criteria were applied iteratively (Step 4–6) and in conjunction with availability assessment. As a result, only 298 texts were retained for full evaluation with 241 originating from scientific databases while 57 were ‘grey’. These studies formed primary texts corpus which was extracted, read in full and evaluated by Relevance Criteria combined with Scoring Metrics . The decision rule was set as follows. Studies that scored “1” or “0” were rejected, while texts with “3” and “2” evaluation were admitted as final primary studies corpus. To this end, as an outcome of SLR-based, broad, cross-domain publications collection and screening we identified 207 relevant publications from peer-reviewed (156 texts) and ‘grey’ literature (51 texts). Figure 5 below exhibits yearly published research numbers with the breakdown by ‘peer-reviewed’ and ‘grey’ literature starting from 1997.

In terms of composition, ‘peer-reviewed’ studies corpus is well-balanced with 72 journal articles and 82 conference papers while book chapters account for 4 instances only. In contrast, in ‘grey’ literature subset, articles in moderated and non-peer reviewed journals are dominant ( n = 34) compared to overall number of conference papers ( n = 13), followed by small number of technical reports and pre-prints ( n = 4).

Temporal analysis of texts corpus (as per Fig. 5 below) resulted in two observations. Firstly, we note that stable and significant research interest (in terms of numbers) on data mining methodologies application has started around a decade ago—in 2007. Research efforts made prior to 2007 were relatively limited with number of publications below 10. Secondly, we note that research on data mining methodologies has grown substantially since 2007, an observation supported by the 3-year and 10-year constructed mean trendlines. In particular, the number of publications have roughly tripled over past decade hitting all time high with 24 texts released in 2017.

Further, there are also two distinct spike sub-periods in the years 2007–2009 and 2014–2017 followed by stable pattern with overall higher number of released publications on annual basis. This observation is in line with the trend of increased penetration of methodologies, tools, cross-industry applications and academic research of data mining.

Findings and Discussion

In this section, we address the research questions of the paper. Initially, as part of RQ1, we present overview of data mining methodologies ‘as-is’ and adaptation trends. In addressing RQ2, we further classify the adaptations identified. Then, as part of RQ3 subsection, each category identified under RQ2 is analyzed with particular focus on the goals of adaptations.

RQ1: How data mining methodologies are applied (‘as-is’ vs. adapted)?

The first research question examines the extent to which data mining methodologies are used ‘as-is’ versus adapted. Our review based on 207 publications identified two distinct paradigms on how data mining methodologies are applied. The first is ‘as-is’ where the data mining methodologies are applied as stipulated. The second is with ‘adaptations’; that is, methodologies are modified by introducing various changes to the standard process model when applied.

We have aggregated research by decades to differentiate application pattern between two time periods 1997–2007 with limited vs 2008–2018 with more intensive data mining application. The given cut has not only been guided by extracted publications corpus but also by earlier surveys. In particular, during the pre-2007 research, there where ten new methodologies proposed, but since then, only two new methodologies have been proposed. Thus, there is a distinct trend observed over the last decade of large number of extensions and adaptations proposed vs entirely new methodologies.

We note that during the first decade of our time scope (1997–2007), the ratio of data mining methodologies applied ‘as-is’ was 40% (as presented in Fig. 6A ). However, the same ratio for the following decade is 32% ( Fig. 6B ). Thus, in terms of relative shares we note a clear decrease in using data mining methodologies ‘as-is’ in favor of adapting them to cater to specific needs.The trend is even more pronounced when comparing numbers—adaptations more than tripled (from 30 to 106) while ‘as-is’ scenario has increased modestly (from 20 to 51). Given this finding, we continue with analyzing how data mining methodologies have been adapted under RQ2.

RQ2: How have existing data mining methodologies been adapted?

We identified that data mining methodologies have been adapted to cater to specific needs. In order to categorize adaptations scenarios, we applied a two-level dichotomy, specifically, by applying the following decision tree:

• Level 1 Decision: Has the methodology been combined with another methodology? If yes, the resulting methodology was classified in the ‘integration’ category. Otherwise, we posed the next question.
• Level 2 Decision: Are any new elements (phases, tasks, deliverables) added to the methodology? If yes, we designate the resulting methodology as an ‘extension’ of the original one. Otherwise, we classify the resulting methodology as a modification of the original one.

Thus, when adapted three distinct types of adaptation scenarios can be distinguished:

• Scenario ‘Modification’: introduces specialized sub-tasks and deliverables in order to address specific use cases or business problems. Modifications typically concentrate on granular adjustments to the methodology at the level of sub-phases, tasks or deliverables within the existing reference frameworks (e.g., CRISP-DM or KDD) stages. For example, Chernov et al. (2014) , in the study of mobile network domain, proposed automated decision-making enhancement in the deployment phase. In addition, the evaluation phase was modified by using both conventional and own-developed performance metrics. Further, in a study performed within the financial services domain, Yang et al. (2016) presents feature transformation and feature selection as sub-phases, thereby enhancing the data mining modeling stage.
• Scenario ‘Extension’: primarily proposes significant extensions to reference data mining methodologies. Such extensions result in either integrated data mining solutions, data mining frameworks serving as a component or tool for automated IS systems, or their transformations to fit specialized environments. The main purposes of extensions are to integrate fully-scaled data mining solutions into IS/IT systems and business processes and provide broader context with useful architectures, algorithms, etc. Adaptations, where extensions have been made, elicit and explicitly present various artifacts in the form of system and model architectures, process views, workflows, and implementation aspects. A number of soft goals are also achieved, providing holistic perspective on data mining process, and contextualizing with organizational needs. Also, there are extensions in this scenario where data mining process methodologies are substantially changed and extended in all key phases to enable execution of data mining life-cycle with the new (Big) Data technologies, tools and in new prototyping and deployment environments (e.g., Hadoop platforms or real-time customer interfaces). For example, Kisilevich, Keim & Rokach (2013) presented extensions to traditional CRISP-DM data mining outcomes with fully fledged Decision Support System (DSS) for hotel brokerage business. Authors ( Kisilevich, Keim & Rokach, 2013 ) have introduced spatial/non-spatial data management (extending data preparation), analytical and spatial modeling capabilities (extending modeling phase), provided spatial display and reporting capabilities (enhancing deployment phase). In the same work domain knowledge was introduced in all phases of data mining process, and usability and ease of use were also addressed.
• Scenario ‘Integration’: combines reference methodology, for example, CRISP-DM with: (1) data mining methodologies originated from other domains (e.g., Software engineering development methodologies), (2) organizational frameworks (Balanced Scorecard, Analytics Canvass, etc.), or (3) adjustments to accommodate Big Data technologies and tools. Also, adaptations in the form of ‘Integration’ typically introduce various types of ontologies and ontology-based tools, domain knowledge, software engineering, and BI-driven framework elements. Fundamental data mining process adjustments to new types of data, IS architectures (e.g., real time data, multi-layer IS) are also presented. Key gaps addressed with such adjustments are prescriptive nature and low degree of formalization in CRISP-DM, obsolete nature of CRISP-DM with respect to tools, and lack of CRISP-DM integration with other organizational frameworks. For example, Brisson & Collard (2008) developed KEOPS data mining methodology (CRIPS-DM based) centered on domain knowledge integration. Ontology-driven information system has been proposed with integration and enhancements to all steps of data mining process. Further, an integrated expert knowledge used in all data mining phases was proved to produce value in data mining process.

To examine how the application scenario of each data mining methodology usage has developed over time, we mapped peer-reviewed texts and ‘grey’ literature to respective adaptation scenarios, aggregated by decades (as presented in the Fig. 7 for peer-reviewed and Fig. 8 for ‘grey’).

For peer-reviewed research, such temporal analysis resulted in three observations. Firstly, research efforts in each adaptation scenario has been growing and number of publication more than quadrupled (128 vs. 28). Secondly, as noted above relative proportion of ‘as-is’ studies is diluted (from 39% to 33%) and primarily replaced with ‘Extension’ paradigm (from 25% to 30%). In contrast, in relative terms ‘Modification’ and ‘Integration’ paradigms gains are modest. Further, this finding is reinforced with other observation—most notable gaps in terms of modest number of publications remain in ‘Integration’ category where excluding 2008–2009 spike, research efforts are limited and number of texts is just 13. This is in stark contrast with prolific research in ‘Extension category’ though concentrated in the recent years. We can hypothesize that existing reference methodologies do not accommodate and support increasing complexity of data mining projects and IS/IT infrastructure, as well as certain domains specifics and as such need to be adapted.

In ‘grey’ literature, in contrast to peer-reviewed research, growth in number of publications is less profound—29 vs. 22 publications or 32% comparing across two decade (as per Fig. 8 ). The growth is solely driven by ‘Integration’ scenarios application (13 vs. 4 publications) while both ‘as-is’ and other adaptations scenarios are stagnating or in decline.

RQ3: For what purposes have existing data mining methodologies been adapted?

We address the third research question by analyzing what gaps the data mining methodology adaptations seek to fill and the benefits of such adaptations. We identified three adaptation scenarios, namely ‘Modification’, ‘Extension’, and ‘Integration’. Here, we analyze each of them.

Modification

Modifications of data mining methodologies are present in 30 peer-reviewed and 4 ‘grey’ literature studies. The analysis shows that modifications overwhelmingly consist of specific case studies. However, the major differentiating point compared to ‘as-is’ case studies is clear presence of specific adjustments towards standard data mining process methodologies. Yet, the proposed modifications and their purposes do not go beyond traditional data mining methodologies phases. They are granular, specialized and executed on tasks, sub-tasks, and at deliverables level. With modifications, authors describe potential business applications and deployment scenarios at a conceptual level, but typically do not report or present real implementations in the IS/IT systems and business processes.

Further, this research subcategory can be best classified based on domains where case studies were performed and data mining methodologies modification scenarios executed. We have identified four distinct domain-driven applications presented in the Fig. 9 .

IT, IS domain

The largest number of publications (14 or app. 40%), was performed on IT, IS security, software development, specific data mining and processing topics. Authors address intrusion detection problem in Hossain, Bridges & Vaughn (2003) , Fan, Ye & Chen (2016) , Lee, Stolfo & Mok (1999) , specialized algorithms for variety of data types processing in Yang & Shi (2010) , Chen et al. (2001) , Yi, Teng & Xu (2016) , Pouyanfar & Chen (2016) , effective and efficient computer and mobile networks management in Guan & Fu (2010) , Ertek, Chi & Zhang (2017) , Zaki & Sobh (2005) , Chernov, Petrov & Ristaniemi (2015) , Chernov et al. (2014) .

Manufacturing and engineering

The next most popular research area is manufacturing/engineering with 10 case studies. The central topic here is high-technology manufacturing, for example, semi-conductors associated—study of Chien, Diaz & Lan (2014) , and various complex prognostics case studies in rail, aerospace domains ( Létourneau et al., 2005 ; Zaluski et al., 2011 ) concentrated on failure predictions. These are complemented by studies on equipment fault and failure predictions and maintenance ( Kumar, Shankar & Thakur, 2018 ; Kang et al., 2017 ; Wang, 2017 ) as well as monitoring system ( García et al., 2017 ).

Sales and services, incl. financial industry

The third category is presented by seven business application papers concerning customer service, targeting and advertising ( Karimi-Majd & Mahootchi, 2015 ; Reutterer et al., 2017 ; Wang, 2017 ), financial services credit risk assessments ( Smith, Willis & Brooks, 2000 ), supply chain management ( Nohuddin et al., 2018 ), and property management ( Yu, Fung & Haghighat, 2013 ), and similar.

As a consequence of specialization, these studies concentrate on developing ‘state-of-the art’ solution to the respective domain-specific problem.

‘Extension’ scenario was identified in 46 peer-reviewed and 12 ‘grey’ publications. We noted that ‘Extension’ to existing data mining methodologies were executed with four major purposes:

• Purpose 1: To implement fully scaled, integrated data mining solution and regular, repeatable knowledge discovery process— address model, algorithm deployment, implementation design (including architecture, workflows and corresponding IS integration). Also, complementary goal is to tackle changes to business process to incorporate data mining into organization activities.
• Purpose 2: To implement complex, specifically designed systems and integrated business applications with data mining model/solution as component or tool. Typically, this adaptation is also oriented towards Big Data specifics, and is complemented by proposed artifacts such as Big Data architectures, system models, workflows, and data flows.
• Purpose 3: To implement data mining as part of integrated/combined specialized infrastructure, data environments and types (e.g., IoT, cloud, mobile networks) .
• Purpose 4: To incorporate context-awareness aspects.

The specific list of studies mapped to each of the given purposes presented in the Appendix ( Table A1 ). Main purposes of adaptations, associated gaps and/or benefits along with observations and artifacts are documented in the Fig. 10 below.

In ‘Extension’ category, studies executed with the Purpose 1 propose fully scaled, integrated data mining solutions of specific data mining models, associated frameworks and processes. The distinctive trait of this research subclass is that it ensures repeatability and reproducibility of delivered data mining solution in different organizational and industry settings. Both the results of data mining use case as well as deployment and integration into IS/IT systems and associated business process(es) are presented explicitly. Thus, ‘Extension’ subclass is geared towards specific solution design, tackling concrete business or industrial setting problem or addressing specific research gaps thus resembling comprehensive case study.

This direction can be well exemplified by expert finder system in research social network services proposed by Sun et al. (2015) , data mining solution for functional test content optimization by Wang (2015) and time-series mining framework to conduct estimation of unobservable time-series by Hu et al. (2010) . Similarly, Du et al. (2017) tackle online log anomalies detection, automated association rule mining is addressed by Çinicioğlu et al. (2011) , software effort estimation by Deng, Purvis & Purvis (2011) , network patterns visual discovery by Simoff & Galloway (2008) . Number of studies address solutions in IS security ( Shin & Jeong, 2005 ), manufacturing ( Güder et al., 2014 ; Chee, Baharudin & Karkonasasi, 2016 ), materials engineering domains ( Doreswamy, 2008 ), and business domains ( Xu & Qiu, 2008 ; Ding & Daniel, 2007 ).

In contrast, ‘Extension’ studies executed for the Purpose 2 concentrate on design of complex, multi-component information systems and architectures. These are holistic, complex systems and integrated business applications with data mining framework serving as component or tool. Moreover, data mining methodology in these studies is extended with systems integration phases.

For example, Mobasher (2007) presents data mining application in Web personalization system and associated process; here, data mining cycle is extended in all phases with utmost goal of leveraging multiple data sources and using discovered models and corresponding algorithms in an automatic personalization system. Authors comprehensively address data processing, algorithm, design adjustments and respective integration into automated system. Similarly, Haruechaiyasak, Shyu & Chen (2004) tackle improvement of Webpage recommender system by presenting extended data mining methodology including design and implementation of data mining model. Holistic view on web-mining with support of all data sources, data warehousing and data mining techniques integration, as well as multiple problem-oriented analytical outcomes with rich business application scenarios (personalization, adaptation, profiling, and recommendations) in e-commerce domain was proposed and discussed by Büchner & Mulvenna (1998) . Further, Singh et al. (2014) tackled scalable implementation of Network Threat Intrusion Detection System. In this study, data mining methodology and resulting model are extended, scaled and deployed as module of quasi-real-time system for capturing Peer-to-Peer Botnet attacks. Similar complex solution was presented in a series of publications by Lee et al. (2000 , 2001) who designed real-time data mining-based Intrusion Detection System (IDS). These works are complemented by comprehensive study of Barbará et al. (2001) who constructed experimental testbed for intrusion detection with data mining methods. Detection model combining data fusion and mining and respective components for Botnets identification was developed by Kiayias et al. (2009) too. Similar approach is presented in Alazab et al. (2011) who proposed and implemented zero-day malware detection system with associated machine-learning based framework. Finally, Ahmed, Rafique & Abulaish (2011) presented multi-layer framework for fuzzy attack in 3G cellular IP networks.

A number of authors have considered data mining methodologies in the context of Decision Support Systems and other systems that generate information for decision-making, across a variety of domains. For example, Kisilevich, Keim & Rokach (2013) executed significant extension of data mining methodology by designing and presenting integrated Decision Support System (DSS) with six components acting as supporting tool for hotel brokerage business to increase deal profitability. Similar approach is undertaken by Capozzoli et al. (2017) focusing on improving energy management of properties by provision of occupancy pattern information and reconfiguration framework. Kabir (2016) presented data mining information service providing improved sales forecasting that supported solution of under/over-stocking problem while Lau, Zhang & Xu (2018) addressed sales forecasting with sentiment analysis on Big Data. Kamrani, Rong & Gonzalez (2001) proposed GA-based Intelligent Diagnosis system for fault diagnostics in manufacturing domain. The latter was tackled further in Shahbaz et al. (2010) with complex, integrated data mining system for diagnosing and solving manufacturing problems in real time.

Lenz, Wuest & Westkämper (2018) propose a framework for capturing data analytics objectives and creating holistic, cross-departmental data mining systems in the manufacturing domain. This work is representative of a cohort of studies that aim at extending data mining methodologies in order to support the design and implementation of enterprise-wide data mining systems. In this same research cohort, we classify Luna, Castro & Romero (2017) , which presents a data mining toolset integrated into the Moodle learning management system, with the aim of supporting university-wide learning analytics.

One study addresses multi-agent based data mining concept. Khan, Mohamudally & Babajee (2013) have developed unified theoretical framework for data mining by formulating a unified data mining theory. The framework is tested by means of agent programing proposing integration into multi-agent system which is useful due to scalability, robustness and simplicity.

The subcategory of ‘Extension’ research executed with Purpose 3 is devoted to data mining methodologies and solutions in specialized IT/IS, data and process environments which emerged recently as consequence of Big Data associated technologies and tools development. Exemplary studies include IoT associated environment research, for example, Smart City application in IoT presented by Strohbach et al. (2015) . In the same domain, Bashir & Gill (2016) addressed IoT-enabled smart buildings with the additional challenge of large amount of high-speed real time data and requirements of real-time analytics. Authors proposed integrated IoT Big Data Analytics framework. This research is complemented by interdisciplinary study of Zhong et al. (2017) where IoT and wireless technologies are used to create RFID-enabled environment producing analysis of KPIs to improve logistics.

Significant number of studies addresses various mobile environments sometimes complemented by cloud-based environments or cloud-based environments as stand-alone. Gomes, Phua & Krishnaswamy (2013) addressed mobile data mining with execution on mobile device itself; the framework proposes innovative approach addressing extensions of all aspects of data mining including contextual data, end-user privacy preservation, data management and scalability. Yuan, Herbert & Emamian (2014) and Yuan & Herbert (2014) introduced cloud-based mobile data analytics framework with application case study for smart home based monitoring system. Cuzzocrea, Psaila & Toccu (2016) have presented innovative FollowMe suite which implements data mining framework for mobile social media analytics with several tools with respective architecture and functionalities. An interesting paper was presented by Torres et al. (2017) who addressed data mining methodology and its implementation for congestion prediction in mobile LTE networks tackling also feedback reaction with network reconfigurations trigger.

Further, Biliri et al. (2014) presented cloud-based Future Internet Enabler—automated social data analytics solution which also addresses Social Network Interoperability aspect supporting enterprises to interconnect and utilize social networks for collaboration. Real-time social media streamed data and resulting data mining methodology and application was extensively discussed by Zhang, Lau & Li (2014) . Authors proposed design of comprehensive ABIGDAD framework with seven main components implementing data mining based deceptive review identification. Interdisciplinary study tackling both these topics was developed by Puthal et al. (2016) who proposed integrated framework and architecture of disaster management system based on streamed data in cloud environment ensuring end-to-end security. Additionally, key extensions to data mining framework have been proposed merging variety of data sources and types, security verification and data flow access controls. Finally, cloud-based manufacturing was addressed in the context of fault diagnostics by Kumar et al. (2016) .

Also, Mahmood et al. (2013) tackled Wireless Sensor Networks and associated data mining framework required extensions. Interesting work is executed by Nestorov & Jukic (2003) addressing rare topic of data mining solutions integration within traditional data warehouses and active mining of data repositories themselves.

Supported by new generation of visualization technologies (including Virtual Reality environments), Wijayasekara, Linda & Manic (2011) proposed and implemented CAVE-SOM (3D visual data mining framework) which offers interactive, immersive visual data mining with multiple visualization modes supported by plethora of methods. Earlier version of visual data mining framework was successfully developed and presented by Ganesh et al. (1996) as early as in 1996.

Large-scale social media data is successfully tackled by Lemieux (2016) with comprehensive framework accompanied by set of data mining tools and interface. Real time data analytics was addressed by Shrivastava & Pal (2017) in the domain of enterprise service ecosystem. Images data was addressed in Huang et al. (2002) by proposing multimedia data mining framework and its implementation with user relevance feedback integration and instance learning. Further, exploded data diversity and associated need to extend standard data mining is addressed by Singh et al. (2016) in the study devoted to object detection in video surveillance systems supporting real time video analysis.

Finally, there is also limited number of studies which addresses context awareness (Purpose 4) and extends data mining methodology with context elements and adjustments. In comparison with ‘Integration’ category research, here, the studies are at lower abstraction level, capturing and presenting list of adjustments. Singh, Vajirkar & Lee (2003) generate taxonomy of context factors, develop extended data mining framework and propose deployment including detailed IS architecture. Context-awareness aspect is also addressed in the papers reviewed above, for example, Lenz, Wuest & Westkämper (2018) , Kisilevich, Keim & Rokach (2013) , Sun et al. (2015) , and other studies.

Integration

‘Integration’ of data mining methodologies scenario was identified in 27 ‘peer-reviewed’ and 17 ‘grey’ studies. Our analysis revealed that this adaptation scenario at a higher abstraction level is typically executed with the five key purposes:

• Purpose 1: to integrate/combine with various ontologies existing in organization .
• Purpose 2: to introduce context-awareness and incorporate domain knowledge .
• Purpose 3: to integrate/combine with other research or industry domains framework, process methodologies and concepts .
• Purpose 4: to integrate/combine with other well-known organizational governance frameworks, process methodologies and concepts .
• Purpose 5: to accommodate and/or leverage upon newly available Big Data technologies, tools and methods.

The specific list of studies mapped to each of the given purposes presented in Appendix ( Table A2 ). Main purposes of adaptations, associated gaps and/or benefits along with observations and artifacts are documented in Fig. 11 below.

As mentioned, number of studies concentrates on proposing ontology-based Integrated data mining frameworks accompanies by various types of ontologies (Purpose 1). For example, Sharma & Osei-Bryson (2008) focus on ontology-based organizational view with Actors, Goals and Objectives which supports execution of Business Understanding Phase. Brisson & Collard (2008) propose KEOPS framework which is CRISP-DM compliant and integrates a knowledge base and ontology with the purpose to build ontology-driven information system (OIS) for business and data understanding phases while knowledge base is used for post-processing step of model interpretation. Park et al. (2017) propose and design comprehensive ontology-based data analytics tool IRIS with the purpose to align analytics and business. IRIS is based on concept to connect dots, analytics methods or transforming insights into business value, and supports standardized process for applying ontology to match business problems and solutions.

Further, Ying et al. (2014) propose domain-specific data mining framework oriented to business problem of customer demand discovery. They construct ontology for customer demand and customer demand discovery task which allows to execute structured knowledge extraction in the form of knowledge patterns and rules. Here, the purpose is to facilitate business value realization and support actionability of extracted knowledge via marketing strategies and tactics. In the same vein, Cannataro & Comito (2003) presented ontology for the Data Mining domain which main goal is to simplify the development of distributed knowledge discovery applications. Authors offered to a domain expert a reference model for different kind of data mining tasks, methodologies, and software capable to solve the given business problem and find the most appropriate solution.

Apart from ontologies, Sharma & Osei-Bryson (2009) in another study propose IS inspired, driven by Input-Output model data mining methodology which supports formal implementation of Business Understanding Phase. This research exemplifies studies executed with Purpose 2. The goal of the paper is to tackle prescriptive nature of CRISP-DM and address how the entire process can be implemented. Cao, Schurmann & Zhang (2005) study is also exemplary in terms of aggregating and introducing several fundamental concepts into traditional CRISP-DM data mining cycle—context awareness, in-depth pattern mining, human–machine cooperative knowledge discovery (in essence, following human-centricity paradigm in data mining), loop-closed iterative refinement process (similar to Agile-based methodologies in Software Development). There are also several concepts, like data, domain, interestingness, rules which are proposed to tackle number of fundamental constrains identified in CRISP-DM. They have been discussed and further extended by Cao & Zhang (2007 , 2008) , Cao (2010) into integrated domain driven data mining concept resulting in fully fledged D3M (domain-driven) data mining framework. Interestingly, the same concepts, but on individual basis are investigated and presented by other authors, for example, context-aware data mining methodology is tackled by Xiang (2009a , 2009b) in the context of financial sector. Pournaras et al. (2016) attempted very crucial privacy-preservation topic in the context of achieving effective data analytics methodology. Authors introduced metrics and self-regulatory (reconfigurable) information sharing mechanism providing customers with controls for information disclosure.

A number of studies have proposed CRISP-DM adjustments based on existing frameworks, process models or concepts originating in other domains (Purpose 3), for example, software engineering ( Marbán et al., 2007 , 2009 ; Marban, Mariscal & Segovia, 2009 ) and industrial engineering ( Solarte, 2002 ; Zhao et al., 2005 ).

Meanwhile, Mariscal, Marbán & Fernández (2010) proposed a new refined data mining process based on a global comparative analysis of existing frameworks while Angelov (2014) outlined a data analytics framework based on statistical concepts. Following a similar approach, some researchers suggest explicit integration with other areas and organizational functions, for example, BI-driven Data Mining by Hang & Fong (2009) . Similarly, Chen, Kazman & Haziyev (2016) developed an architecture-centric agile Big Data analytics methodology, and an architecture-centric agile analytics and DevOps model. Alternatively, several authors tackled data mining methodology adaptations in other domains, for example, educational data mining by Tavares, Vieira & Pedro (2017) , decision support in learning management systems ( Murnion & Helfert, 2011 ), and in accounting systems ( Amani & Fadlalla, 2017 ).

Other studies are concerned with actionability of data mining and closer integration with business processes and organizational management frameworks (Purpose 4). In particular, there is a recurrent focus on embedding data mining solutions into knowledge-based decision making processes in organizations, and supporting fast and effective knowledge discovery ( Bohanec, Robnik-Sikonja & Borstnar, 2017 ).

Examples of adaptations made for this purpose include: (1) integration of CRISP-DM with the Balanced Scorecard framework used for strategic performance management in organizations ( Yun, Weihua & Yang, 2014 ); (2) integration with a strategic decision-making framework for revenue management Segarra et al. (2016) ; (3) integration with a strategic analytics methodology Van Rooyen & Simoff (2008) , and (4) integration with a so-called ‘Analytics Canvas’ for management of portfolios of data analytics projects Kühn et al. (2018) . Finally, Ahangama & Poo (2015) explored methodological attributes important for adoption of data mining methodology by novice users. This latter study uncovered factors that could support the reduction of resistance to the use of data mining methodologies. Conversely, Lawler & Joseph (2017) comprehensively evaluated factors that may increase the benefits of Big Data Analytics projects in an organization.

Lastly, a number of studies have proposed data mining frameworks (e.g., CRISP-DM) adaptations to cater for new technological architectures, new types of datasets and applications (Purpose 5). For example, Lu et al. (2017) proposed a data mining system based on a Service-Oriented Architecture (SOA), Zaghloul, Ali-Eldin & Salem (2013) developed a concept of self-service data analytics, Osman, Elragal & Bergvall-Kåreborn (2017) blended CRISP-DM into a Big Data Analytics framework for Smart Cities, and Niesen et al. (2016) proposed a data-driven risk management framework for Industry 4.0 applications.

Our analysis of RQ3, regarding the purposes of existing data mining methodologies adaptations, revealed the following key findings. Firstly, adaptations of type ‘Modification’ are predominantly targeted at addressing problems that are specific to a given case study. The majority of modifications were made within the domain of IS security, followed by case studies in the domains of manufacturing and financial services. This is in clear contrast with adaptations of type ‘Extension’, which are primarily aimed at customizing the methodology to take into account specialized development environments and deployment infrastructures, and to incorporate context-awareness aspects. Thirdly, a recurrent purpose of adaptations of type ‘Integration’ is to combine a data mining methodology with either existing ontologies in an organization or with other domain frameworks, methodologies, and concepts. ‘Integration’ is also used to instill context-awareness and domain knowledge into a data mining methodology, or to adapt it to specialized methods and tools, such as Big Data. The distinctive outcome and value (gaps filled in) of ‘Integrations’ stems from improved knowledge discovery, better actionability of results, improved combination with key organizational processes and domain-specific methodologies, and improved usage of Big Data technologies.

We discovered that the adaptations of existing data mining methodologies found in the literature can be classified into three categories: modification, extension, or integration.

We also noted that adaptations are executed either to address deficiencies and lack of important elements or aspects in the reference methodology (chiefly CRISP-DM). Furthermore, adaptations are also made to improve certain phases, deliverables or process outcomes.

In short, adaptations are made to:

• improve key reference data mining methodologies phases—for example, in case of CRISP-DM these are primarily business understanding and deployment phases.
• support knowledge discovery and actionability.
• introduce context-awareness and higher degree of formalization.
• integrate closer data mining solution with key organizational processes and frameworks.
• significantly update CRISP-DM with respect to Big Data technologies, tools, environments and infrastructure.
• incorporate broader, explicit context of architectures, algorithms and toolsets as integral deliverables or supporting tools to execute data mining process.
• expand and accommodate broader unified perspective for incorporating and implementing data mining solutions in organization, IT infrastructure and business processes.

Threats to Validity

Systematic literature reviews have inherent limitations that must be acknowledged. These threats to validity include subjective bias (internal validity) and incompleteness of search results (external validity).

The internal validity threat stems from the subjective screening and rating of studies, particularly when assessing the studies with respect to relevance and quality criteria. We have mitigated these effects by documenting the survey protocol (SLR Protocol), strictly adhering to the inclusion criteria, and performing significant validation procedures, as documented in the Protocol.

The external validity threat relates to the extent to which the findings of the SLR reflect the actual state of the art in the field of data mining methodologies, given that the SLR only considers published studies that can be retrieved using specific search strings and databases. We have addressed this threat to validity by conducting trial searches to validate our search strings in terms of their ability to identify relevant papers that we knew about beforehand. Also, the fact that the searches led to 1,700 hits overall suggests that a significant portion of the relevant literature has been covered.

In this study, we have examined the use of data mining methodologies by means of a systematic literature review covering both peer-reviewed and ‘grey’ literature. We have found that the use of data mining methodologies, as reported in the literature, has grown substantially since 2007 (four-fold increase relative to the previous decade). Also, we have observed that data mining methodologies were predominantly applied ‘as-is’ from 1997 to 2007. This trend was reversed from 2008 onward, when the use of adapted data mining methodologies gradually started to replace ‘as-is’ usage.

The most frequent adaptations have been in the ‘Extension’ category. This category refers to adaptations that imply significant changes to key phases of the reference methodology (chiefly CRISP-DM). These adaptations particularly target the business understanding, deployment and implementation phases of CRISP-DM (or other methodologies). Moreover, we have found that the most frequent purposes of adaptions are: (1) adaptations to handle Big Data technologies, tools and environments (technological adaptations); and (2) adaptations for context-awareness and for integrating data mining solutions into business processes and IT systems (organizational adaptations). A key finding is that standard data mining methodologies do not pay sufficient attention to deployment aspects required to scale and transform data mining models into software products integrated into large IT/IS systems and business processes.

Apart from the adaptations in the ‘Extension’ category, we have also identified an increasing number of studies focusing on the ‘Integration’ of data mining methodologies with other domain-specific and organizational methodologies, frameworks, and concepts. These adaptions are aimed at embedding the data mining methodology into broader organizational aspects.

Overall, the findings of the study highlight the need to develop refinements of existing data mining methodologies that would allow them to seamlessly interact with IT development platforms and processes (technological adaptation) and with organizational management frameworks (organizational adaptation). In other words, there is a need to frame existing data mining methodologies as being part of a broader ecosystem of methodologies, as opposed to the traditional view where data mining methodologies are defined in isolation from broader IT systems engineering and organizational management methodologies.

Supplemental Information

Supplemental information 1.

Unfortunately, we were not able to upload any graph (original png files). Based on Overleaf placed PeerJ template we constructed graphs files based on the template examples. Unfortunately, we were not able to understand why it did not fit, redoing to new formats will change all texts flow and generated pdf file. We submit graphs in archived file as part of supplementary material. We will do our best to redo the graphs further based on instructions from You.

Supplemental Information 2

File starts with Definitions page—it lists and explains all columns definitions as well as SLR scoring metrics. Second page contains"Peer reviewed" texts while next one "grey" literature corpus.

Funding Statement

The authors received no funding for this work.

Additional Information and Declarations

The authors declare that they have no competing interests.

Veronika Plotnikova conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the paper, and approved the final draft.

Marlon Dumas conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft.

Fredrik Milani conceived and designed the experiments, authored or reviewed drafts of the paper, and approved the final draft.

Primary Sources

Application of Spatiotemporal Data Mining to Air Quality Data Page: Title Page

This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided to UNT Digital Library by the UNT Libraries .

View a full description of this thesis .

• Adjust Image
• Rotate Left
• Rotate Right
• Brightness, Contrast, etc. (Experimental)
• Cropping Tool
• Download Sizes

Preview all sizes/dimensions or...

• Download Thumbnail
• Download Small
• Download Medium
• Download Large
• High Resolution Files
• IIIF Image JSON
• IIIF Image URL
• Accessibility
• View Extracted Text

Extracted Text

The following text was automatically extracted from the image on this page using optical character recognition software:

APPLICATION OF SPATIOTEMPORAL DATA MINING TO AIR QUALITY DATA

Michael Anthony Biancardi Thesis Prepared for the Degree of MASTER OF SCIENCE UNIVERSITY OF NORTH TEXAS May 2024 APPROVED:

Yan Huang, Major Professor Lu Liang, Co-Major Professor Xiaohui Yuan, Committee Member Gergely Zdruba, Chair of the Department of Computer Science and Engineering Paul Krueger, Dean of the College of Engineering Victor Prybutok, Dean of the Toulouse Graduate School

Upcoming Pages

Here’s what’s next.

Show all pages in this thesis .

Search Inside

This thesis can be searched. Note: Results may vary based on the legibility of text within the document.

• or search this site for other thesis or dissertations

Tools / Downloads

Get a copy of this page or view the extracted text.

View Extracted (OCR) Text

Citing and Sharing

Basic information for referencing this web page. We also provide extended guidance on usage rights, references, copying or embedding.

Reference the current page of this Thesis.

Biancardi, Michael Anthony. Application of Spatiotemporal Data Mining to Air Quality Data , thesis , May 2024; Denton, Texas . ( https://digital.library.unt.edu/ark:/67531/metadc2332660/m1/1/ : accessed July 3, 2024 ), University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu ; . Copy Citation

Print / Share This Page

• Embed Viewer

Permanent URL (This Page)

Univesal viewer, international image interoperability framework (this page).

Have a language expert improve your writing

Check your paper for plagiarism in 10 minutes, generate your apa citations for free.

• Knowledge Base
• Using AI tools

What Is Data Mining? | Definition & Techniques

Published on July 20, 2023 by Kassiani Nikolopoulou .

Data mining is the process of extracting meaningful information from vast amounts of data. With data mining methods, organizations can discover hidden patterns, relationships, and trends in data, which they can use to solve business problems, make predictions, and increase their profits or efficiency.

The term “data mining” is actually a misnomer because the goal is not to extract the data itself, but rather meaningful information from the data .

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

What is data mining, what are different data mining techniques, how does data mining work, data mining application examples, other interesting articles, frequently asked questions.

Data mining, also known as knowledge discovery in data (KDD) , is a branch of data science that brings together computer software, machine learning (i.e., the process of teaching machines how to learn from data without human intervention), and statistics to extract or mine useful information from massive data sets.

Through our online interactions with companies, government agencies, or educational institutes, we produce a large amount of data. This “big data” consists of data sets so large that it’s not possible for a human to analyze them. Instead, this is done with the assistance of a computer.

Data mining transforms this raw data into practical knowledge that helps organizations answer important questions about their users or consumers. Data mining applications include consumer behavior analysis, sales forecasting, and fraud detection.

Don't submit your assignments before you do this

The academic proofreading tool has been trained on 1000s of academic texts. Making it the most accurate and reliable proofreading tool for students. Free citation check included.

Try for free

Data mining techniques draw from various fields like machine learning (ML) and statistics . Here are a few common data mining techniques:

• Classification is the task of assigning new data to known or predefined categories. For example, sorting a data set consisting of emails as “spam” or “not spam.”
• Clustering is the process of grouping data that share common characteristics into subgroups or clusters. Unlike classification (where groups are predefined), clustering is a discovery technique that helps us identify patterns. This allows businesses to create customer segments based on loyalty, communication preferences, or any other trait that emerges from the data.
• Association rule learning is a technique that looks for relationships between data points. A grocery store chain may use association rule learning to find out which products are frequently bought together and use these insights for promotions.
• Regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. The goal is to predict the value of the dependent variable based on the values of the independent variables . For example, using historical data about houses with similar characteristics, we might predict the future value of a house.
• Anomaly or outlier detection is the process of identifying unusual data within a data set (i.e., data that doesn’t follow the general pattern). This data may be interesting (e.g., if it signals a spike in the sales of certain products) or may need further investigation (e.g., if it indicates potential instances of fraud).

The data mining process involves using statistical methods and machine learning algorithms to identify patterns in data. Thanks to advancements in computer processing power and speed, analyzing data is largely automated.

Although there are different ways to describe the data mining process, a widely used model is the Cross-Industry Standard Process for Data Mining (CRISP-DM) , which includes the following stages:

Business understanding

Data understanding, data preparation, data modeling.

In the business understanding stage, we need to identify the problem we intend to solve through data mining (e.g., how to create a more targeted marketing campaign).

Data scientists and other relevant stakeholders need to define the business problem, which will inform the questions that guide the project. Additional research might be necessary to understand the business context. Determining project goals and success criteria is important for collecting the right data and evaluating the project’s outcomes.

Once the business problem is defined, we need to determine the type of data needed and identify relevant sources. In this step, data scientists collect data from various sources, such as transaction records and customer databases.

However, not every data point may be relevant for the project. For example, a company may only be interested in purchases via credit card. The goal here is to ensure that only the necessary data will be included. By the end of the data understanding stage, the data mining team should have selected the subset of data necessary to address the problem.

Data preparation is the most time-consuming stage and involves several actions to get the data ready for further processing and analysis. This may involve excluding duplicates, missing data , or outliers from the data (i.e., data cleansing ).

Data from multiple sources may be merged, organized, or adjusted in different ways to prepare for the next phase. At the end of this stage, the data mining team has identified the most relevant variables and prepared the final data set.

Data modeling is the process of organizing and understanding data in a structured way. It helps data mining teams find meaningful patterns and insights in the available data.

Data scientists use different models depending on the type of data they have and the problem they’re trying to solve. For example, they might want to identify which products are often purchased together or detect suspicious transactions in banks. To do this, they may use different techniques.

For example, they may apply classification techniques to categorize labeled data or use clustering techniques to group similar data points together. By iterating through this modeling process, data scientists try to reach the best solution.

They build models that group customers into segments that reflect shared travel interests and characteristics. They find out that their customers mainly consist of three distinct groups: “adventure seekers,” “cultural explorers,” and “family vacationers.” Note There are two main types of data: labeled and unlabeled .

• Labeled data means that it has been manually annotated with specific information (e.g., emails labeled “spam” or “not spam”). In this case, data scientists can use a supervised machine learning approach , where the model learns from these labeled examples to make predictions on new, unseen data.
• On the other hand, if the data is unlabeled , data scientists can use unsupervised machine learning , which helps them discover patterns and relationships within the data without any predefined labels.

During the evaluation stage , the data mining team begins to assess the model’s effectiveness in answering their initial question. This is a human-driven phase, as the project leader needs to decide if the model answers the original question well or uncovers new and previously unknown patterns.

Unlike the technical assessment in the modeling phase, the evaluation phase involves determining which model best meets the objectives and deciding how to proceed. This involves evaluating the results against success criteria, reviewing the process for any oversights, and summarizing findings.

The team may decide, for example, to move on to the next phase or, if the model does not align with the desired objectives, to explore alternative models or revisit the data.

The deployment step is about putting the knowledge and insights gathered from the project into practical use.

Depending on the original question or problem, deployment can be something simple like creating a report or a visual presentation, or something more complex like generating a new sales strategy. Deployment involves integrating the results into the organization’s operations or decision-making process.

Here are some real-world examples of data mining:

• Market basket analysis. Retailers use data mining to analyze large data sets and discover consumers’ buying patterns, such as items that are frequently bought together or seasonal trends. They can use this information to better organize their physical stores or websites, predict sales, and promote deals
• Academic research. In the field of literary studies, data mining techniques can be used to analyze texts and understand the emotions expressed by authors or characters. Sentiment analysis (or opinion mining) involves using natural language processing and machine learning algorithms to determine the emotional tone of a text.
• Education . Educational data mining (EDM) aims to improve learning by analyzing a variety of educational data, such as students’ interactions with online learning environments or administrative data from schools and universities. This method can help education providers understand what students need and support them better (e.g., through customized lessons or by identifying and engaging with at-risk students before they drop out).

Check for common mistakes

Use the best grammar checker available to check for common mistakes in your text.

Fix mistakes for free

If you want to know more about ChatGPT , AI tools , fallacies , and research bias , make sure to check out some of our other articles with explanations and examples.

• ChatGPT vs human editor
• University policies on AI
• Machine learning
• Using ChatGPT for your studies
• Sunk cost fallacy
• Straw man fallacy
• Slippery slope fallacy
• Red herring fallacy
• Ecological fallacy
• Logical fallacy

Research bias

• Implicit bias
• Framing bias
• Cognitive bias
• Optimism bias
• Hawthorne effect
• Unconscious bias

Data mining and data analysis are often used interchangeably. However, they are two distinct processes in the field of data science.

• Data mining is the process of uncovering hidden patterns, trends, or relationships in large data sets. It involves various techniques like machine learning and statistics , to find useful information in complex data and support decision-making and planning. This process is also called “knowledge discovery.”
• Data analysis , on the other hand, is a broader term that describes the entire process of inspecting, cleaning, and organizing raw data. The goal is to draw conclusions, make inferences, and support decision-making. Data analysis includes various techniques like descriptive statistics , data mining, hypothesis testing , and regression analysis .

In other words, data mining is one of the techniques used for data analysis when there is a need to uncover hidden patterns and relationships in the data that other methods might miss, while data analysis encompasses a wider range of activities.

Data mining is important because it allows us to discover meaningful patterns and relationships in large volumes of data in a relatively quick and efficient way.

Data mining techniques can take advantage of data coming from different sources like social media platforms or customer databases and convert it into useful insights. In turn, these can answer business or research questions , make predictions, and inform decision making.

Data mining and machine learning are related fields, but they have different purposes:

• The goal of machine learning is to develop algorithms that allow computers to learn without human intervention. It’s about making machines smarter, so they can carry out tasks related to human intelligence independently.
• The goal of data mining is to sift through large data sets and extract useful information like patterns and relationships that can be used to support decision-making. In other words, it’s a tool for humans.

While data mining and machine learning have distinct goals, there is some overlap in their applications. Machine learning can be used as a means to conduct data mining by automatically detecting patterns in data. On the other hand, data gathered from data mining can be used to teach machines and improve their learning capabilities.

In short, data mining and machine learning can complement each other, but they are distinct in their purposes and applications.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

Nikolopoulou, K. (2023, July 20). What Is Data Mining? | Definition & Techniques. Scribbr. Retrieved July 2, 2024, from https://www.scribbr.com/ai-tools/data-mining/

Yağcı, M. (2022). Educational data mining: prediction of students’ academic performance using machine learning algorithms. Smart Learning Environments, 9 (1). https://doi.org/10.1186/s40561-022-00192-z

Is this article helpful?

Kassiani Nikolopoulou

Other students also liked, what is generative ai | meaning & examples, how to write good chatgpt prompts, easy introduction to reinforcement learning.

Kassiani Nikolopoulou (Scribbr Team)

Thanks for reading! Hope you found this article helpful. If anything is still unclear, or if you didn’t find what you were looking for here, leave a comment and we’ll see if we can help.

Still have questions?

"i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

You are using an outdated browser. Please upgrade your browser .

T4Tutorials.com

Data mining research topics for ms phd.

I am sharing with you some of the research topics regarding data mining that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree.

Categorizing the research into 4 categories in this tutorial

Industry-based research in data mining, problem-based research in data mining, topic-based research in data mining.

• 900+ research ideas in data mining

List of some famous Industries in the world for industry-based research in data mining

• Automobile Wholesaling
• Pharmaceuticals Wholesaling
• Life Insurance & Annuities
• Online Computer Software Sales
• Supermarkets & Grocery Stores
• Electric Power Transmission
• IT Consulting
• Wholesale Trade Agents and Brokers
• Retirement & Pension Plans
• Petroleum Refining
• New Car Dealers
• Drug, Cosmetic & Toiletry Wholesaling
• Pharmacy Benefit Management
• Property, Casualty and Direct Insurance
• Colleges & Universities
• Public Schools
• Warehouse Clubs & Supercenters
• Health & Medical Insurance
• Gasoline & Petroleum Wholesaling
• Gasoline & Petroleum Bulk Stations
• Commercial Banking
• Real Estate Loans & Collateralized Debt
• E-Commerce & Online Auctions
• Electronic Part & Equipment Wholesaling

List of some problems for research in data mining.

• Crime Rate Prediction
• Fraud Detection
• Website Evaluation
• Market Analysis
• Financial Analysis
• Customer trend analysis
• Data Warehouse and DBMS
• Multidimensional data model
• OLAP operations
• Example: loan data set
• Data cleaning
• Data transformation
• Data reduction
• Discretization and generating concept hierarchies
• Installing Weka 3 Data Mining System
• Experiments with Weka – filters, discretization
• Task relevant data
• Background knowledge
• Interestingness measures
• Representing input data and output knowledge
• Visualization techniques
• Experiments with Weka – visualization
• Attribute generalization
• Attribute relevance
• Class comparison
• Statistical measures
• Experiments with Weka – using filters and statistics
• Motivation and terminology
• Example: mining weather data
• Basic idea: item sets
• Generating item sets and rules efficiently
• Correlation analysis
• Experiments with Weka – mining association rules
• Basic learning/mining tasks
• Inferring rudimentary rules: 1R algorithm
• Decision trees
• Covering rules
• Experiments with Weka – decision trees, rules
• The prediction task
• Statistical (Bayesian) classification
• Bayesian networks
• Instance-based methods (nearest neighbor)
• Linear models
• Experiments with Weka – Prediction
• Basic issues in clustering
• First conceptual clustering system: Cluster/2
• Partitioning methods: k-means, expectation-maximization (EM)
• Hierarchical methods: distance-based agglomerative and divisible clustering
• Conceptual clustering: Cobweb
• Experiments with Weka – k-means, EM, Cobweb
• Text mining: extracting attributes (keywords), structural approaches (parsing, soft parsing).
• Bayesian approach to classifying text
• Web mining: classifying web pages, extracting knowledge from the web
• Data Mining software and applications

Research Topics Computer Science

Topic Covered

Top 10 research topics of Data Mining | list of research topics of Data Mining | trending research topics of Data Mining | research topics for dissertation in Data Mining | dissertation topics of Data Mining in pdf | dissertation topics in Data Mining | research area of interest Data Mining | example of research paper topics in Data Mining | top 10 research thesis topics of Data Mining | list of research thesis  topics of Data Mining| trending research thesis topics of Data Mining | research thesis  topics for dissertation in Data Mining | thesis topics of Data Mining in pdf | thesis topics in Data Mining | examples of thesis topics of Data Mining | PhD research topics examples of  Data Mining | PhD research topics in Data Mining | PhD research topics in computer science | PhD research topics in software engineering | PhD research topics in information technology | Masters (MS) research topics in computer science | Masters (MS) research topics in software engineering | Masters (MS) research topics in information technology | Masters (MS) thesis topics in Data Mining.

Related Posts:

• What is data mining? What is not data mining?
• Data Stream Mining - Data Mining
• Data Quality in Data Preprocessing for Data Mining
• Frequent pattern Mining, Closed frequent itemset, max frequent itemset in data mining
• Semantic Web Research Topics for MS PhD
• Network Security Research Topics for MS PhD

UKnowledge > College of Engineering > Mining Engineering > Theses & Dissertations

Theses and Dissertations--Mining Engineering

Theses/dissertations from 2024 2024.

THE METHODOLOGY FOR INTEGRATING ROBOTIC SYSTEMS IN UNDEGROUND MINING MACHINES , Peter Kolapo

DISCRETE ELEMENT MODELING TO PREDICT MUCKPILE PROFILES FROM CAST BLASTING , Russell Lamont

AUTONOMOUS SHUTTLE CAR DOCKING TO A CONTINUOUS MINER USING RGB-DEPTH IMAGERY , Sky Rose

Theses/Dissertations from 2023 2023

ASSESSMENT OF AIR OVERPRESSURE FROM BLASTING USING COMPUTATIONAL FLUID DYNAMICS , Cecilia Estefania Aramayo

RECOVERY OF VALUABLE METALS FROM ELECTRONIC WASTE USING A NOVEL AMMONIA-BASED HYDROMETALLURGICAL PROCESS , Peijia Lin

AN ACID BAKING APPROACH TO ENHANCE RARE EARTH ELEMENT RECOVERY FROM BITUMINOUS COAL SOURCES , Ahmad Nawab

PREDICTION OF DYNAMIC SUBSIDENCE IN THE PROXIMITY OF LONGWALL PANEL BOUNDARIES , JESUS DAVID ROMERO BENITEZ

Prediction of Blast-Induced Ground Vibrations: A Comparison Between Empirical and Artificial-Neural-Network Approaches , Luis F. Velasquez

A LABORATORY AND NUMERICAL INVESTIGATION OF THE STRENGTH OF IRREGULARLY SHAPED PILLARS , Zachary Wedding

Theses/Dissertations from 2022 2022

DEVELOPMENT OF UNIVARIATE AND MULTIVARIATE FORECASTING MODELS FOR METHANE GAS EMISSIONS IN UNDERGROUND COAL MINES , Juan Diaz

PARAMETRIC NUMERICAL ANALYSIS OF INCLINED COAL PILLARS , Robin Flattery

Strain Energy Analysis Related To Strata Failure During Caving Operations , Caroline Gerwig

LAPTOP RECYCLING CASE STUDY: ESTIMATING THE CONTAINED VALUE AND VALUE RECOVERY PROCESS FEASIBILITY OF END-OF-LIFE CONSUMER ELECTRONICS , Zebulon Hart

INVESTIGATION INTO, & ANALYSIS OF TEMPERATURE & STRAIN DATA FOR COAL MINE SEAL MATERIAL DURING CURING , Stephanus Jaco van den Berg

Theses/Dissertations from 2021 2021

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES , Vasileios Androulakis

Investigation of Coal Burst Potential Using Numerical Modeling and Rock Burst Indices , Cristian David Cardenas Triana

Capture of Respirable Dust using Maintenance Free Impingement Screen , Neeraj Kumar Gupta

OXIDATION PRETREATMENT FOR ENHANCED LEACHABILITY OF RARE EARTH ELEMENTS FROM BITUMINOUS COAL SOURCES , Tushar Gupta

AN APPROACH FOR PREDICTING FLOW CHARACTERISTICS AT THE CONTINUOUS MINER FACE , Kayla Henderson

CONCEPTS FOR DEVELOPMENT OF SHUTTLE CAR AUTONOMOUS DOCKING WITH CONTINUOUS MINER USING 3-D DEPTH CAMERA , Sibley Miller

MODELING OF RARE EARTH SOLVENT EXTRACTION PROCESS FOR FLOWSHEET DESIGN AND OPTIMIZATION , Vaibhav Kumar Srivastava

Application of a Novel Ventilation Simplification Algorithm , Caitlin V. Strong

A METHODOLOGY FOR AUTONOMOUS ROOF BOLT INSTALLATION USING INDUSTRIAL ROBOTICS , Anastasia Xenaki

Theses/Dissertations from 2020 2020

NUMERICAL APPROXIMATION OF THE GROUND REACTION AND SUPPORT REACTION CURVES FOR UNDERGROUND LIMESTONE MINES , Jesus Castillo Gomez

Advanced Search

• Notify me via email or RSS

Browse by Author

• Collections
• Disciplines

Author Corner

• Submit Research

New Title Here

Below. --> connect.

• Law Library
• Special Collections
• Copyright Resource Center
• Graduate School
• Scholars@UK

• We’d like your feedback

Home | About | FAQ | My Account | Accessibility Statement

Privacy Copyright

University of Kentucky ®

An Equal Opportunity University Accreditation Directory Email Privacy Policy Accessibility Disclosures

• Advanced Search

Digitization Workflow for Data Mining in Production Technology applied to a Feed Axis of a CNC Milling Machine

New citation alert added.

This alert has been successfully added and will be sent to:

You will be notified whenever a record that you have chosen has been cited.

To manage your alert preferences, click on the button below.

New Citation Alert!

Please log in to your account

Information & Contributors

Bibliometrics & citations, view options, recommendations, a summary of research on frequent itemsets mining technology.

In recent years, communication technology and Internet technology are developing rapidly. In this context, people pay more and more attention to the importance of data, especially the implied information with timeliness and value, and accessing to such ...

Mining fuzzy specific rare itemsets for education data

Association rule mining is an important data analysis method for the discovery of associations within data. There have been many studies focused on finding fuzzy association rules from transaction databases. Unfortunately, in the real world, one may ...

Large-scale linear nonparallel support vector machine solver

Twin support vector machines (TWSVMs), as the representative nonparallel hyperplane classifiers, have shown the effectiveness over standard SVMs from some aspects. However, they still have some serious defects restricting their further study and real ...

Information

Published in.

Elsevier Science Publishers B. V.

Netherlands

Publication History

Author tags.

• Condition Monitoring
• Machine Learning
• Digitization
• Production Engineering
• Production Technology
• Anomaly Detection
• Classification
• Usable artificial Intelligence
• Data Mining
• Research-article

Contributors

Other metrics, bibliometrics, article metrics.

• 0 Total Citations
• 0 Total Downloads
• Downloads (Last 12 months) 0
• Downloads (Last 6 weeks) 0

View options

Login options.

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Share this publication link.

Copying failed.

Share on social media

Affiliations, export citations.

• Please download or close your previous search result export first before starting a new bulk export. Preview is not available. By clicking download, a status dialog will open to start the export process. The process may take a few minutes but once it finishes a file will be downloadable from your browser. You may continue to browse the DL while the export process is in progress. Download
• Download citation
• Copy citation

We are preparing your search results for download ...

We will inform you here when the file is ready.

Your file of search results citations is now ready.

Your search export query has expired. Please try again.

Help | Advanced Search

Computer Science > Computation and Language

Title: evaluating the ability of llms to solve semantics-aware process mining tasks.

Abstract: The process mining community has recently recognized the potential of large language models (LLMs) for tackling various process mining tasks. Initial studies report the capability of LLMs to support process analysis and even, to some extent, that they are able to reason about how processes work. This latter property suggests that LLMs could also be used to tackle process mining tasks that benefit from an understanding of process behavior. Examples of such tasks include (semantic) anomaly detection and next activity prediction, which both involve considerations of the meaning of activities and their inter-relations. In this paper, we investigate the capabilities of LLMs to tackle such semantics-aware process mining tasks. Furthermore, whereas most works on the intersection of LLMs and process mining only focus on testing these models out of the box, we provide a more principled investigation of the utility of LLMs for process mining, including their ability to obtain process mining knowledge post-hoc by means of in-context learning and supervised fine-tuning. Concretely, we define three process mining tasks that benefit from an understanding of process semantics and provide extensive benchmarking datasets for each of them. Our evaluation experiments reveal that (1) LLMs fail to solve challenging process mining tasks out of the box and when provided only a handful of in-context examples, (2) but they yield strong performance when fine-tuned for these tasks, consistently surpassing smaller, encoder-based language models.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

HOW TO CABIN THE REALIST INDETERMINACY THESIS: ON GREEN, POSITIVISM, AND THE SOURCES OF LAW

To appear in a collection of essays on the philosophy of Leslie Green, edited by T. Adams, K. Greasley, and D. Reaume (Oxford University Press, forthcoming)

21 Pages Posted:

Brian Leiter

University of Chicago

Date Written: July 01, 2024

Leslie Green raised an important challenge to my reconstruction of the American Legal Realist (ALR) arguments for the indeterminacy of law and legal reasoning:  how can those arguments be limited, as I claim, to mostly appellate cases?  The key, I argue, is to recognize that (1) the central ALR argument for indeterminacy appeals to the existence of equally "legitimate" but conflicting ways of interpreting valid sources of law, and (2) the relevant notion of "legitimacy" is sociological (i.e., what is actually accepted by lawyers and judges).  The ALR argument for indeterminacy being most apparent at the appellate level is then an empirical claim, which the ALRs supported with extensive evidence in many areas of law.  I also consider Green's suggestion that ALR takes most sources to be "permissive sources" (in Hart's sense), and criticize some misunderstandings of both ALR and Scandinavian Realism. 

Suggested Citation: Suggested Citation

Brian Leiter (Contact Author)

University of chicago ( email ).

1111 E. 60th St. Chicago, IL 60637 United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics, related ejournals, jurisprudence & legal philosophy ejournal.

Subscribe to this fee journal for more curated articles on this topic

Philosophy of Law eJournal

Subscribe to this free journal for more curated articles on this topic

Journal of Materials Chemistry B

Integrated platform for decoding hydrophilic peptide fingerprints of hepatocellular carcinoma using artificial intelligence and two-dimensional nanosheets.

Hydrophilic peptides (HPs) play a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, the comprehensive and in-depth high-throughput analysis of specific changes in HPs associated with HCC remains unrealized, due to the complex nature of biological fluids and the challenges of mining complex patterns in large data sets. The clinical diagnosis of HCC still lacks a non-destructive and accurate classification method, given the limited specificity of widely used biomarkers. To address these challenges, we have established a multifunctional platform that integrates artificial intelligence computation, hydrophilic interaction extraction of HPs, and MALDI-MS testing. This platform aims to achieve highly sensitive HPs fingerprinting for accurate diagnosis of HCC. The method not only facilitates efficient detection of HPs, but also achieves a remarkable 100.00% diagnostic accuracy for HCC in test cohort, supported by machine learning algorithms. By constructing a panel of HPs with 10 characteristic features, we achieved 98% accuracy in test cohort for rapid diagnosis and identified 62 HPs deeply involved in pathways related to liver diseases. This integrated strategy provides new research directions for future biomarker studies as well as early diagnosis and individualized treatment of HCC.

• This article is part of the themed collection: Journal of Materials Chemistry B HOT Papers

Supplementary files

• Supplementary information PDF (1696K)

Article information

Download citation, permissions.

Z. Li, B. Ma, S. Shui, Z. Tu, W. Peng, Y. Chen, J. Zhou, F. Lan, B. Ying and Y. Wu, J. Mater. Chem. B , 2024, Accepted Manuscript , DOI: 10.1039/D4TB00700J

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page .

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page .

Read more about how to correctly acknowledge RSC content .

Social activity

Search articles by author.

This article has not yet been cited.

Advertisements