| Issue |
RAIRO-Oper. Res.
Volume 42, Number 1, January-March 2008
Cooperative methods for multiobjective optimization
|
|
|---|---|---|
| Page(s) | 69 - 83 | |
| DOI | https://doi.org/10.1051/ro:2008004 | |
| Published online | 21 February 2008 | |
Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery
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
Received:
30
October
2006
Accepted:
30
October
2007
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.
Mathematics Subject Classification: 90Cxx / 68XX
Key words: Hybridization / multi-objective optimization / knowledge discovery / association rules
© EDP Sciences, ROADEF, SMAI, 2008
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