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Combining Evolutionary Algorithms and exact ...
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Document type :
Article dans une revue scientifique
DOI :
10.1051/ro:2008004
Title :
Combining Evolutionary Algorithms and exact approaches for multi-objective knowledge discovery
Author(s) :
Khabzaoui, Mohammed [Auteur]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Dhaenens, Clarisse [Auteur] refId
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Talbi, El-Ghazali [Auteur] refId
Parallel Cooperative Multi-criteria Optimization [DOLPHIN]
Laboratoire d'Informatique Fondamentale de Lille [LIFL]
Journal title :
RAIRO - Operations Research
Pages :
69-83
Publisher :
EDP Sciences
Publication date :
2008
ISSN :
0399-0559
English keyword(s) :
Hybridization
multi-objective optimization
knowledge discovery
association rules
HAL domain(s) :
Informatique [cs]/Recherche opérationnelle [cs.RO]
English abstract : [en]
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 ...
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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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
Source :
Harvested from HAL
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