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

Mohammed Khabzaoui 1 Clarisse Dhaenens 1, 2 El-Ghazali Talbi 1, 2
1 LIFL - Laboratoire d'Informatique Fondamentale de Lille
2 DOLPHIN - Parallel Cooperative Multi-criteria Optimization
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe
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 : Hybridization multi-objective optimization knowledge discovery association rules
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Article dans une revue
RAIRO - Operations Research, EDP Sciences, 2008, 42, pp.69-83. 〈10.1051/ro:2008004〉
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Contributeur : Clarisse Dhaenens <>
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Dernière modification le : jeudi 11 janvier 2018 - 06:22:13
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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〉

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