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, Volume 17, Issue 2, pp 115–139 | Cite as

Metaheuristics for data mining

Survey and opportunities for big data
  • Clarisse Dhaenens
  • Laetitia JourdanEmail author
Invited Survey

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 

Mathematics Subject Classification

90-02 68-02 

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.CRIStAL - Centre de Recherche en Informatique Signal et Automatique de LilleUniv. Lille, CNRS, Centrale Lille, UMR 9189LilleFrance

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