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Author: search.filters.author.Nouali-Taboudjemat, NadiaSubject: search.filters.subject.Association Rule Mining

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    An Efficient Measure for Evaluating Association Rules
    (CERIST, 2014-06-24) Djenouri, Youcef; Gheraibai, Youcef; Mehdi, Malika; Bendjoudi, Ahcène; Nouali-Taboudjemat, Nadia
    Association rules mining (ARM) has attracted a lot of attention in the last decade. It aims to extract a set of relevant rules from a given database. In order to evaluate the quality of the resulting rules, existing measures, such as support and confidence, allow to evaluate the resulted rules of ARM process separately, missing the different dependencies between the rules. This paper addresses the problem of evaluating rules by taking into account two aspects : (1) The accuracy of the returned rules on the input data and (2) the distance between the returned rules. The rules set that covers the maximum of rules space is considered. To analyze the behavior of the proposed measure, it has been tested on two recent ARM algorithms BSO-ARM and HBSO-TS.
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    Parallel Association Rules Mining Using GPUs and Bees Behaviors
    (CERIST, 2014-06-24) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Nouali-Taboudjemat, Nadia; Habbas, Zineb
    This 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.
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    Parallel Rules Mining Using GPUs and Bees Behaviors
    (2014-08) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Nouali-Taboudjemat, Nadia; Habbas, Zineb
    This 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.