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

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

Iman Raeesi Vanani, Mir Seyed Mohammad Mohsen Emamat
Copyright: © 2019 |Pages: 27
ISBN13: 9781522551379|ISBN10: 1522551379|ISBN13 Softcover: 9781522586746|EISBN13: 9781522551386
DOI: 10.4018/978-1-5225-5137-9.ch003
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MLA

Vanani, Iman Raeesi, and Mir Seyed Mohammad Mohsen Emamat. "Analytical Review of the Applications of Multi-Criteria Decision Making in Data Mining." Optimizing Big Data Management and Industrial Systems With Intelligent Techniques, edited by Sultan Ceren Öner and Oya H. Yüregir, IGI Global, 2019, pp. 53-79. https://doi.org/10.4018/978-1-5225-5137-9.ch003

APA

Vanani, I. R. & Emamat, M. S. (2019). Analytical Review of the Applications of Multi-Criteria Decision Making in Data Mining. In S. Öner & O. Yüregir (Eds.), Optimizing Big Data Management and Industrial Systems With Intelligent Techniques (pp. 53-79). IGI Global. https://doi.org/10.4018/978-1-5225-5137-9.ch003

Chicago

Vanani, Iman Raeesi, and Mir Seyed Mohammad Mohsen Emamat. "Analytical Review of the Applications of Multi-Criteria Decision Making in Data Mining." In Optimizing Big Data Management and Industrial Systems With Intelligent Techniques, edited by Sultan Ceren Öner and Oya H. Yüregir, 53-79. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-5137-9.ch003

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Abstract

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.

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