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Data Allocation Algorithm for Parallel Association Rule Discovery

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Advances in Knowledge Discovery and Data Mining (PAKDD 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2035))

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Abstract

Association rule discovery techniques have gradually been adapt-ed to parallel systems in order to take advantage of the higher speed and greater storage capacity that they offer. The transition to a distributed memory system requires the partitioning of the database among the processors, a procedure that is generally carried out indiscriminately. However, for some techniques the nature of the database partitioning can have a pronounced impact on execution time and attention will be focused on one such algorithm, Fast Parallel Mining (FPM). A new algorithm, Data Allocation Algorithm (DAA), is presented that uses Principal Component Analysis to improve the data distribution prior to FPM.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Manchester, M13 9PL, UK

    Anna M. Manning

  2. Department of Computation, UMIST, Manchester, M60 1QD

    John A. Keane

Authors
  1. Anna M. Manning
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  2. John A. Keane
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Information Systems, The University of Hong Kong, Pokfulam, Hong Kong China

    David Cheung

  2. CSIRO Mathematical and Information Sciences, GPO Box 664, Canberra, ACT 2601, Australia

    Graham J. Williams

  3. Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave., Kowloon, Hong Kong China

    Qing Li

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© 2001 Springer-Verlag Berlin Heidelberg

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Manning, A.M., Keane, J.A. (2001). Data Allocation Algorithm for Parallel Association Rule Discovery. In: Cheung, D., Williams, G.J., Li, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2001. Lecture Notes in Computer Science(), vol 2035. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45357-1_44

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  • DOI: https://doi.org/10.1007/3-540-45357-1_44

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41910-5

  • Online ISBN: 978-3-540-45357-4

  • eBook Packages: Springer Book Archive

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