Economics at your fingertips  

Metaheuristics for data mining

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

4OR, 2019, vol. 17, issue 2, 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
References: Add references at CitEc
Citations: Track citations by RSS feed

Downloads: (external link) Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Ordering information: This journal article can be ordered from
https://www.springer ... ch/journal/10288/PSE

Access Statistics for this article

4OR is currently edited by Yves Crama, Michel Grabisch and Silvano Martello

More articles in 4OR from Springer
Bibliographic data for series maintained by Sonal Shukla ().

Page updated 2019-11-06
Handle: RePEc:spr:aqjoor:v:17:y:2019:i:2:d:10.1007_s10288-019-00402-4