Combining Evolutionary Algorithms and exact approaches for multi-objective knowledge discovery

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.
Type de document :
Article dans une revue
RAIRO - Operations Research, EDP Sciences, 2008, 42, pp.69-83. 〈10.1051/ro:2008004〉
Liste complète des métadonnées

Littérature citée [23 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00269936
Contributeur : Clarisse Dhaenens <>
Soumis le : jeudi 3 avril 2008 - 11:49:37
Dernière modification le : jeudi 11 janvier 2018 - 06:22:13
Document(s) archivé(s) le : vendredi 28 septembre 2012 - 12:15:19

Fichier

RAIRO_08_Dhaenens.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

Citation

Mohammed Khabzaoui, Clarisse Dhaenens, El-Ghazali Talbi. Combining Evolutionary Algorithms and exact approaches for multi-objective knowledge discovery. RAIRO - Operations Research, EDP Sciences, 2008, 42, pp.69-83. 〈10.1051/ro:2008004〉. 〈inria-00269936〉

Partager

Métriques

Consultations de la notice

335

Téléchargements de fichiers

198