Impact of Genetic Algorithms Operators on Association Rules Extraction

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dc.contributor.authorHamdad, Leila
dc.contributor.authorOurnani, Zakaria
dc.contributor.authorBenatchba, Karima
dc.contributor.authorBendjoudi, Ahcène
dc.date.accessioned2016-10-02T09:52:24Z
dc.date.available2016-10-02T09:52:24Z
dc.date.issued2016-10-02
dc.description.abstractIn this paper, we study the impact of GAs’ components such as encoding, different crossover, mutation and replacement strategies on the number of extracted association rules and their quality. Moreover, we propose a strategy to manage the population. The later is organized in classes where each one encloses same size rules. Each class can be seen as a population on which a GA is applied. All tests are conducted on two types of benchmarks : synthetic and real ones of different sizes.fr_FR
dc.identifier.isrnCERIST-DTISI-16-000000015--DZfr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/829
dc.publisherCERIST
dc.relation.ispartofRapports de recherche internes
dc.relation.placeAlger
dc.structureCalcul pervasif et mobile (Pervasive and Mobile Computing group)fr_FR
dc.subjectAssociation rulesfr_FR
dc.subjectGenetic Algorithmfr_FR
dc.subjectPittsburg Algorithmfr_FR
dc.subjectMichigan Algorithmfr_FR
dc.subjectApriori Algorithmfr_FR
dc.titleImpact of Genetic Algorithms Operators on Association Rules Extractionfr_FR
dc.typeTechnical Report
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