Parallel Association Rules Mining Using GPUs and Bees Behaviors

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dc.contributor.authorDjenouri, Youcef
dc.contributor.authorBendjoudi, Ahcène
dc.contributor.authorMehdi, Malika
dc.contributor.authorNouali-Taboudjemat, Nadia
dc.contributor.authorHabbas, Zineb
dc.date.accessioned2014-06-24T09:16:44Z
dc.date.available2014-06-24T09:16:44Z
dc.date.issued2014-06-24
dc.description.abstractThis paper addresses the problem of association rules mining with large data sets using bees behaviors. The bees swarm optimization method have been successfully applied on small and medium data size. Nevertheless, when dealing Webdocs benchmark (the largest benchmark on the web), it is bluntly blocked after more than 15 days. Additionally, Graphic processor Units are massively threaded providing highly intensive computing and very usable by the optimization research community. The parallelization of such method on GPU architecture can be deal large data sets as the case of WebDocs in real time. In this paper, the evaluation process of the solutions is parallelized. Experimental results reveal that the suggested method outperforms the sequential version at the order of ×100 in most data sets, furthermore, the WebDocs benchmark is handled with less than ten hours.fr_FR
dc.identifier.isrnCERIST/DTISI/RR--14-0000000017--dzfr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/675
dc.publisherCERIST
dc.relation.ispartofRapports de recherche internes
dc.relation.placeAlger
dc.structureCalcul Pervasif et Mobilefr_FR
dc.subjectBees Behaviorsfr_FR
dc.subjectAssociation Rule Miningfr_FR
dc.subjectParallel Algorithmsfr_FR
dc.subjectGPU Computingfr_FR
dc.titleParallel Association Rules Mining Using GPUs and Bees Behaviorsfr_FR
dc.typeTechnical Report
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