Analytical Review of the Applications of Multi-Criteria Decision Making in Data Mining

2019 ◽  
pp. 53-79
Author(s):  
Iman Raeesi Vanani ◽  
Mir Seyed Mohammad Mohsen Emamat
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Association Rules ◽  
Significant Part ◽  
Future Research ◽  
Multi Criteria Decision Making ◽  
Research Directions ◽  
Analytical Review ◽  
Future Research Directions ◽  
Reliable Knowledge

In recent years, multi-criteria decision making (MCDM) is a significant part of operations research (OR) and has become an interesting topic to researcher who works in the data mining (DM) field. The aim of this chapter is to provide an in-depth presentation of the contribution of MCDM in the field of DM. In order to develop a reliable knowledge base on accumulating knowledge from previous studies, we present a review of the usage of MCDM methods in DM field. The chapter presents methodology and application. The result shows that the most usage of MCDM in DM consists of evaluating classification algorithms, weighting criteria, and ranking association rules and clusters. Finally, some future research directions are suggested at the end of chapter.

Learning Analytics

2018 ◽  
pp. 5158-5168
Author(s):  
Constanţa-Nicoleta Bodea ◽  
Maria-Iuliana Dascalu ◽  
Radu Ioan Mogos ◽  
Stelian Stancu
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Future Research ◽  
Educational Systems ◽  
Main Section ◽  
Research Directions ◽  
Data Intensive ◽  
Academic Analytics ◽  
Future Research Directions

Reinforcement of the technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The article starts by defining LA, with relevant views on the literature. A discussion about the relationships between LA, educational data mining and academic analytics is included in the background section. In the main section of the article, the learning analytics, as an emerging trend in the educational systems is describe, by discussing the main issues, controversies, problems on this topic. Final part of the article presents the future research directions and the conclusion.

Central Language Hypothesis in the Decision-Making Process

2016 ◽  
pp. 66-83 ◽  
Author(s):  
Duygu Buğa
Keyword(s):  
Decision Making ◽  
Theoretical Framework ◽  
Future Research ◽  
Limbic Cortex ◽  
Decision Making Process ◽  
Discussion Section ◽  
Research Directions ◽  
Additional Information ◽  
Future Research Directions ◽  
The Brain

The purpose of this chapter is to explore the potential connection between neuroeconomics and the Central Language Hypothesis (CLH) which refers to the language placed within the subconscious mind of an individual. The CLH forwards that in the brains of bilingual and multilingual people, one language is more suppressive as it dominates reflexes, emotions, and senses. This central language (CL) is located at the centre of the limbic cortex of the brain. Therefore, when there is a stimulus on the limbic cortex (e.g., fear, anxiety, sadness), the brain produces the central language. The chapter begins with an Introduction followed by a Theoretical Framework. The next section discusses the neurolinguistic projection of the central language and includes the survey and the results used in this study. The Discussion section provides additional information regarding the questionnaire and the CLH, followed by Future Research Directions, Implications, and finally the Conclusion.

Decision Making Under Uncertainty and Risks in the Face of Rapidly Advancing Technologies

2019 ◽  
pp. 38-56
Author(s):  
Vicente González-Prida Díaz ◽  
Jesus Pedro Zamora Bonilla ◽  
Pablo Viveros Gunckel
Keyword(s):  
Decision Making ◽  
Social Perception ◽  
Industry 4.0 ◽  
Future Research ◽  
Risk And Uncertainty ◽  
Research Directions ◽  
The Face ◽  
Future Research Directions ◽  
Key Aspects ◽  
Reduction Of Uncertainty

This chapter aims to consider the effects of the new concept Industry 4.0 on decision making, particularly on the reduction of uncertainty and the risk associated with any choice between alternatives. For this purpose, this chapter begins by dealing with the concepts of risk and uncertainty and their epistemological evolution. After observing certain trends and recent studies in this regard, the authors address a more philosophical perception of risk, mainly on aspects related to engineering and social perception. The concept of human reliability will also be reviewed and how it can be improved with the application of emerging technologies, considering some methodological proposals to improve the decision making. After that, some of the possible future research directions will be briefly discussed. Finally, the chapter concludes by highlighting key aspects of the chapter as a context for other chapters in the book.

Metric Methods in Data Mining

2008 ◽  
pp. 849-879
Author(s):  
Dan A. Simovici
Keyword(s):  
Data Mining ◽  
Metric Space ◽  
Training Data ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Future Research Directions ◽  
Major Data ◽  
Geometric Study

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

Unit of Analysis in Digitally-Enabled Electronic Procurement Research

2020 ◽  
pp. 83-103
Author(s):  
Md Mahbubur Rahim ◽  
Maryam Jabberzadeh ◽  
Nergiz Ilhan
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Critical Issue ◽  
Future Research ◽  
Research Directions ◽  
Research Issues ◽  
Unit Of Analysis ◽  
Electronic Procurement ◽  
Future Research Directions ◽  
Methodological Concern

E-procurement systems that have been in place for over a decade have begun incorporating digital tools like big data, cloud computing, internet of things, and data mining. Hence, there exists a rich literature on earlier e-procurement systems and advanced digitally-enabled e-procurement systems. Existing literature on these systems addresses many research issues (e.g., adoption) associated with e-procurement. However, one critical issue that has so far received no rigorous attention is about “unit of analysis,” a methodological concern of importance, for e-procurement research context. Hence, the aim of this chapter is twofold: 1) to discuss how the notion of “unit of analysis” has been conceptualised in the e-procurement literature and 2) to discuss how its use has been justified by e-procurement scholars to address the research issues under investigation. Finally, the chapter provides several interesting findings and outlines future research directions.

Computational and Data Mining Perspectives on HIV/AIDS in Big Data Era

2022 ◽  
pp. 1477-1503
Author(s):  
Ali Al Mazari
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Sector ◽  
Future Research ◽  
Research Directions ◽  
Scientific Disciplines ◽  
Future Research Directions ◽  
Hiv Aids

HIV/AIDS big data analytics evolved as a potential initiative enabling the connection between three major scientific disciplines: (1) the HIV biology emergence and evolution; (2) the clinical and medical complex problems and practices associated with the infections and diseases; and (3) the computational methods for the mining of HIV/AIDS biological, medical, and clinical big data. This chapter provides a review on the computational and data mining perspectives on HIV/AIDS in big data era. The chapter focuses on the research opportunities in this domain, identifies the challenges facing the development of big data analytics in HIV/AIDS domain, and then highlights the future research directions of big data in the healthcare sector.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

2016 ◽  
pp. 212-244
Author(s):  
Boutheina Fessi ◽  
Yacine Djemaiel ◽  
Noureddine Boudriga
Keyword(s):  
Data Mining ◽  
Dynamic Environments ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Crime Investigation ◽  
Security Investigation ◽  
Future Research Directions ◽  
Digital Investigation

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

2020 ◽  
pp. 700-725
Author(s):  
Boutheina A. Fessi ◽  
Yacine Djemaiel ◽  
Noureddine Boudriga
Keyword(s):  
Data Mining ◽  
Dynamic Environments ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Crime Investigation ◽  
Security Investigation ◽  
Future Research Directions ◽  
Digital Investigation

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

scholarly journals Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development

2020 ◽  
Vol 13 (3) ◽  
pp. 795-848
Author(s):  
Alina Köchling ◽  
Marius Claus Wehner
Keyword(s):  
Systematic Review ◽  
Decision Making ◽  
Current Knowledge ◽  
Future Research ◽  
Journal Articles ◽  
Research Directions ◽  
Current State ◽  
Management Context ◽  
Future Research Directions ◽  
State Of Research

AbstractAlgorithmic decision-making is becoming increasingly common as a new source of advice in HR recruitment and HR development. While firms implement algorithmic decision-making to save costs as well as increase efficiency and objectivity, algorithmic decision-making might also lead to the unfair treatment of certain groups of people, implicit discrimination, and perceived unfairness. Current knowledge about the threats of unfairness and (implicit) discrimination by algorithmic decision-making is mostly unexplored in the human resource management context. Our goal is to clarify the current state of research related to HR recruitment and HR development, identify research gaps, and provide crucial future research directions. Based on a systematic review of 36 journal articles from 2014 to 2020, we present some applications of algorithmic decision-making and evaluate the possible pitfalls in these two essential HR functions. In doing this, we inform researchers and practitioners, offer important theoretical and practical implications, and suggest fruitful avenues for future research.

scholarly journals Virtual bargaining: a theory of social decision-making

2014 ◽  
Vol 369 (1655) ◽  
pp. 20130487 ◽  
Author(s):  
Jennifer B. Misyak ◽  
Nick Chater
Keyword(s):  
Decision Making ◽  
Essential Element ◽  
Social Contexts ◽  
Future Research ◽  
Research Directions ◽  
Social Decision ◽  
Future Research Directions ◽  
Empirical Tests ◽  
Social Decision Making ◽  
Vast Range

An essential element of goal-directed decision-making in social contexts is that agents' actions may be mutually interdependent. However, the most well-developed approaches to such strategic interactions, based on the Nash equilibrium concept in game theory, are sometimes too broad and at other times ‘overlook’ good solutions to fundamental social dilemmas and coordination problems. The authors propose a new theory of social decision-making—virtual bargaining—in which individuals decide among a set of moves on the basis of what they would agree to do if they could openly bargain. The core principles of a formal account are outlined (vis-à-vis the notions of ‘feasible agreement’ and explicit negotiation) and further illustrated with the introduction of a new game, dubbed the ‘Boobytrap game’ (a modification on the canonical Prisoner's Dilemma paradigm). In the first empirical data of how individuals play the Boobytrap game, participants' experimental choices accord well with a virtual bargaining perspective, but do not match predictions from a standard Nash account. Alternative frameworks are discussed, with specific empirical tests between these and virtual bargaining identified as future research directions. Lastly, it is proposed that virtual bargaining underpins a vast range of human activities, from social decision-making to joint action and communication.

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