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

Mohammed Khabzaoui; Clarisse Dhaenens; El-Ghazali Talbi

RAIRO - Operations Research (2008)

  • Volume: 42, Issue: 1, page 69-83
  • ISSN: 0399-0559

Abstract

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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.

How to cite

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Khabzaoui, Mohammed, Dhaenens, Clarisse, and Talbi, El-Ghazali. "Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery." RAIRO - Operations Research 42.1 (2008): 69-83. <http://eudml.org/doc/250395>.

@article{Khabzaoui2008,
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. },
author = {Khabzaoui, Mohammed, Dhaenens, Clarisse, Talbi, El-Ghazali},
journal = {RAIRO - Operations Research},
keywords = {Hybridization; multi-objective optimization; knowledge discovery; association rules; hybridization},
language = {eng},
month = {2},
number = {1},
pages = {69-83},
publisher = {EDP Sciences},
title = {Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery},
url = {http://eudml.org/doc/250395},
volume = {42},
year = {2008},
}

TY - JOUR
AU - Khabzaoui, Mohammed
AU - Dhaenens, Clarisse
AU - Talbi, El-Ghazali
TI - Combining evolutionary algorithms and exact approaches for multi-objective knowledge discovery
JO - RAIRO - Operations Research
DA - 2008/2//
PB - EDP Sciences
VL - 42
IS - 1
SP - 69
EP - 83
AB - 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.
LA - eng
KW - Hybridization; multi-objective optimization; knowledge discovery; association rules; hybridization
UR - http://eudml.org/doc/250395
ER -

References

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