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

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Parallel Processing and Applied Mathematics (PPAM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9573))

Abstract

The 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/worker 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 \(10^{7}\) satisfied rules in only 22 min, while a previous GPU-based approach cannot generate more than \(10^{3}\) satisfied rules into 10 h. The results also reveal that MW-BSO-ARM outperforms the PGARM cluster-based approach in terms of computation time.

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Notes

  1. 1.

    Ibnbadis is a cluster of CERIST research center, Algers, Algeria.

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Author information

Authors and Affiliations

  1. LRDSI, University of Saad Dahleb, Blida, Algeria

    Youcef Djenouri

  2. DTISI, CERIST Research Center, Algiers, Algeria

    Ahcene Bendjoudi & Djamel Djenouri

  3. LCOMS, University of Lorraine, Ile du Saulcy, 57045, Metz Cedex, France

    Zineb Habbas

Authors
  1. Youcef Djenouri
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  2. Ahcene Bendjoudi
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  3. Djamel Djenouri
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  4. Zineb Habbas
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Corresponding author

Correspondence to Youcef Djenouri .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technolog, Czestochowa, Poland

    Roman Wyrzykowski

  2. Department of Computer Science, University of Southern California, Marina Del Rey, California, USA

    Ewa Deelman

  3. Electrical Engineering & Comput. Science, University of Tennessee, Knoxville, Tennessee, USA

    Jack Dongarra

  4. Czestochowa University of Technology, Institute of Computer & Information Sci., Czestochowa, Poland

    Konrad Karczewski

  5. Department of Computer Science, AGH University of Science and Technology, Krakow, Poland

    Jacek Kitowski

  6. Systèmes d’informations, Big Data et Rec, AGH University of Science and Technology, Krakow, Poland

    Kazimierz Wiatr

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Djenouri, Y., Bendjoudi, A., Djenouri, D., Habbas, Z. (2016). Parallel BSO Algorithm for Association Rules Mining Using Master/Worker Paradigm. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds) Parallel Processing and Applied Mathematics. PPAM 2015. Lecture Notes in Computer Science(), vol 9573. Springer, Cham. https://doi.org/10.1007/978-3-319-32149-3_25

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  • DOI: https://doi.org/10.1007/978-3-319-32149-3_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32148-6

  • Online ISBN: 978-3-319-32149-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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