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Author: search.filters.author.Mehdi, MalikaSubject: search.filters.subject.Association rules mining

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    Reducing thread divergence in GPU-based bees swarm optimization applied to association rule mining
    (John Wiley & Sons, Ltd., 2016) Bendjoudi, Ahcène; Djenouri, Youcef; Habbas, Zineb; Mehdi, Malika; Djenouri, Djamel
    The association rules mining (ARM) problem is one of the most important problems in the area of data mining. It aims at finding all relevant association rules from transactional databases. It is CPU time intensive and requires a huge computing power when dealing with large transactional databases. To deal with this issue, Graphics Processing Units (GPUs) are a powerful tool to speed up the search process. However, their performance is closely subject to thread/branch divergence resulting from the single instruction multiple data parallel model of GPUs. In this paper, we propose three approaches based on database reorganization, aiming to reduce thread divergence in GPU-based bees swarm optimization metaheuristic for ARM, respectively, named block-based reordering, transactions-based reordering, and transactions-based reordering with median value. Theoretical and experimental studies have been carried out using well-known large ARM instances. The experiments have been performed on an Intel Xeon 64 bit quad-core processor E5520 coupled to Nvidia Tesla C2075 448 cores. The results show that the proposed approaches minimize considerably the number of thread divergence and improve the overall execution time. Indeed, the number of thread divergence occurrences has been reduced by up to eight times making the execution much faster.
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    An Efficient Measure for Evaluating Association Rules
    (2014-08) Djenouri, Youcef; Gheraibai, Youcef; Mehdi, Malika; Bendjoudi, Ahcène
    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.