Metaheuristics for data mining: survey and opportunities for big data

• Published in: Annals of operations research Vol. 314; no. 1; pp. 117 - 140
• Main Authors: Dhaenens, Clarisse, Jourdan, Laetitia
• Format: Journal Article Book Review
• Language: English
• Published: New York Springer US 01.07.2022; Springer; Springer Nature B.V; Springer Verlag
• Subjects: Analysis; Artificial intelligence; Big Data; Business and Management; Classification; Clustering; Combinatorial analysis; Combinatorics; Computer Science; Data analysis; Data mining; Datasets; Feature selection; Genetic algorithms; Heuristic methods; Knowledge discovery; Machine learning; Management science; Mathematical optimization; Operations Research; Operations Research/Decision Theory; Optimization; Original Research; Theory of Computation; Trends; France; 90-02; Feature selection; Metaheuristics; Classification; Big data; Clustering; Association rules; 68-02
• ISSN: 0254-5330; 1572-9338
• DOI: 10.1007/s10479-021-04496-0

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 in Metaheuristics for big data, Wiley, Hoboken, 2016), and an article published in 4OR (Dhaenens and Jourdan in 4OR 17 (2):115–139, 2019); additionally, updated references and an analysis of the current trends are presented.