RAIRO - Operations Research

Table of Contents - January 2008 - Volume 42, Issue 01 (Cooperative methods for multiobjective optimization)  

Research Article

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

Mohammed Khabzaouia1, Clarisse Dhaenensa1 and El-Ghazali Talbia1

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

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.

(Received October 30 2006)

(Accepted October 30 2007)

(Online publication February 21 2008)

Key Words:

  • Hybridization;
  • multi-objective optimization;
  • knowledge discovery;
  • association rules

Mathematics Subject Classification:

  • 90Cxx;
  • 68XX