Parallel BSO Algorithm for Association Rules Mining Using Master/Worker Paradigm

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dc.contributor.authorDjenouri, Youcef
dc.contributor.authorBendjoudi, Ahcène
dc.contributor.authorDjenouri, Djamel
dc.contributor.authorHabbas, Zineb
dc.date.accessioned2016-05-15T10:25:41Z
dc.date.available2016-05-15T10:25:41Z
dc.date.issued2015-09-06
dc.description.abstractThe extraction of association rules from large transactional databases is considered in the paper using cluster architecture parallel computing. Motivated by both the successful sequential BSO-ARM algorithm, and the strong matching between this algorithm and the structure of the cluster architectures, we present in this paper a new parallel ARM algorithm that we call MW-BSO-ARM for Master/Workers version of BSO-ARM. The goal is to deal with large databases by minimizing the communication and synchronization costs, which represent the main challenges that faces any cluster architecture. The experimental results are very promising and show clear improvement that reaches 300% for large instances. For examples, in big transactional database such as WebDocs, the proposed approach generates 107 satisfied rules in only 22 minutes, while a previous GPU-based approach cannot generate more than 103 satisfied rules into 10 hours. The results also reveal that MWBSO-ARM outperforms the PGARM cluster-based approach in terms of computation time.fr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/807
dc.relation.ispartofseries11th International Conference on Parallel Processing and Applied Mathematics;
dc.relation.pages258-268fr_FR
dc.relation.placeKrakow (Poland)fr_FR
dc.structureCalcul pervasif et mobile (Pervasive and Mobile Computing group)fr_FR
dc.subjectGPU computingfr_FR
dc.subjectBees swarm optimizationfr_FR
dc.subjectAssociation rules miningfr_FR
dc.titleParallel BSO Algorithm for Association Rules Mining Using Master/Worker Paradigmfr_FR
dc.typeConference paper
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