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Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery

Published online by Cambridge University Press:  21 February 2008

Mohammed Khabzaoui ,
Clarisse Dhaenens  and
El-Ghazali Talbi
Mohammed Khabzaoui
Affiliation:
Polytech'Lille, LIFL – CNRS / INRIA – Bâtiment M3, University of Lille 1, 59655 Villeneuve d'Ascq Cedex, France; Clarisse.Dhaenens@lifl.fr; El-Ghazali.Talbi@lifl.fr
Clarisse Dhaenens
Affiliation:
Polytech'Lille, LIFL – CNRS / INRIA – Bâtiment M3, University of Lille 1, 59655 Villeneuve d'Ascq Cedex, France; Clarisse.Dhaenens@lifl.fr; El-Ghazali.Talbi@lifl.fr
El-Ghazali Talbi
Affiliation:
Polytech'Lille, LIFL – CNRS / INRIA – Bâtiment M3, University of Lille 1, 59655 Villeneuve d'Ascq Cedex, France; Clarisse.Dhaenens@lifl.fr; El-Ghazali.Talbi@lifl.fr
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Abstract

An important task of knowledge discovery deals with discovering association rules. This very general model has been widely studied and efficient algorithms have been proposed. But most of the time, only frequent rules are seeked. Here we propose to consider this problem as a multi-objective combinatorial optimization problem in order to be able to also find non frequent but interesting rules. As the search space may be very large, a discussion about different approaches is proposed and a hybrid approach that combines a metaheuristic and an exact operator is presented.

Keywords

Hybridizationmulti-objective optimizationknowledge discoveryassociation rules
Type
Research Article
Information
RAIRO - Operations Research , Volume 42 , Issue 1: Cooperative methods for multiobjective optimization , January 2008 , pp. 69 - 83
Copyright
© EDP Sciences, ROADEF, SMAI, 2008

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