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Item Parallel BSO Algorithm for Association Rules Mining using Master/Workers Paradigm(CERIST, 2015-07-07) Djenouri, Youcef; Bendjoudi, Ahcène; Djenouri, DjamelThe 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.Item Data reordering for minimizing threads divergence in GPU-based evaluating association rules(CERIST, 2015-03-26) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Habbas, Zineb; Nouali-Taboudjemat, NadiaThis last decade, the success of Graphics Processor Units (GPUs) has led researchers to launch a lot of works on solving large complex problems by using these cheap and powerful architecture. Association Rules Mining (ARM) is one of these hard problems requiring a lot of computational resources. Due to the exponential increase of data bases size, existing algorithms for ARM problem become more and more inefficient. Thus, research has been focusing on parallelizing these algorithms. Recently, GPUs are starting to be used to this task. However, their major drawback is the threads divergence problem. To deal with this issue, we propose in this paper an intelligent strategy called Transactions- based Reordering "TR" allowing an efficient evaluation of association rules on GPU by minimizing threads divergence. This strategy is based on data base re-organization. To validate our proposition, theoretical and experimental studies have been carried out using well-known synthetic data sets. The results are very promising in terms of minimizing the number of threads divergence.Item Data reordering for minimizing threads divergence in GPU-based evaluating association rules(2015-06) Djenouri, Youcef; Bendjoudi, Ahcène; Mehdi, Malika; Habbas, Zineb; Nouali-Taboudjemat, NadiaThis last decade, the success of Graphics Processor Units (GPUs) has led researchers to launch a lot of works on solving large complex problems by using these cheap and powerful architecture. Association Rules Mining (ARM) is one of these hard problems requiring a lot of computational resources. Due to the exponential increase of data bases size, existing algorithms for ARM problem become more and more inefficient.Thus, research has been focusing on parallelizing these algorithms. Recently, GPUs are starting to be used to this task. However, their major drawback is the threads divergence problem. To deal with this issue, we propose in this paper an intelligent strategy called transactions-based Reordering ”TR” allowing an efficient evaluation of association rules on GPU by minimizing threads divergence. This strategy is based on data base re-organization. To validate our proposition, theoretical and experimental studies have been carried out using well-known synthetic datasets. The results are very promising in terms of minimizing the number of threads divergence.