Metaheuristics for data mining

Clarisse Dhaenens () and Laetitia Jourdan ()
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Clarisse Dhaenens: Univ. Lille, CNRS, Centrale Lille, UMR 9189
Laetitia Jourdan: Univ. Lille, CNRS, Centrale Lille, UMR 9189

4OR, 2019, vol. 17, issue 2, No 1, 115-139

Abstract: Abstract In the context of big data, many scientific communities aim to provide efficient approaches to accommodate large-scale datasets. This is the case of the machine-learning community, and more generally, the artificial intelligence community. The aim of this article is to explain how data mining problems can be considered as combinatorial optimization problems, and how metaheuristics can be used to address them. Four primary data mining tasks are presented: clustering, association rules, classification, and feature selection. This article follows the publication of a book in 2016 concerning this subject (Dhaenens and Jourdan, Metaheuristics for big data, Wiley, New York, 2016); additionally, updated references and an analysis of the current trends are presented.

Keywords: Metaheuristics; Clustering; Association rules; Classification; Feature selection; Big data; 90-02; 68-02 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s10288-019-00402-4

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