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Efficient Parallel Algorithms for Mining Associations

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Large-Scale Parallel Data Mining

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1759))

Abstract

The problem of mining hidden associations present in the large amounts of data has seen widespread applications in many practical domains such as customer-oriented planning and marketing, telecommunication network monitoring, and analyzing data from scientific experiments. The combinatorial complexity of the problem and phenomenal growth in the sizes of available datasets motivate the need for efficient and scalable parallel algorithms. The design of such algorithms is challenging. This chapter presents an evolutionary and comparative review of many existing representative serial and parallel algorithms for discovering two kinds of associations. The first part of the chapter is devoted to the non-sequential associations, which utilize the relationships between events that happen together. The second part is devoted to the more general and potentially more useful sequential associations, which utilize the temporal or sequential relationships between events. It is shown that many existing algorithms actually belong to a few categories which are decided by the broader design strategies. Overall the aim of the chapter is to provide a comprehensive account of the challenges and issues involved in effective parallel formulations of algorithms for discovering associations, and how various existing algorithms try to handle them.

This work was supported by NSF grant ACI-9982274, by Army High Performance Computing Research Center cooperative agreement number DAAH04-95-2-0003/contract number DAAH04-95-C-0008, the content of which does not necessarily reflect the position or the policy of the government, and no official endorsement should be inferred. Access to computing facilities was provided by AHPCRC, Minnesota Supercomputer Institute. Related papers are available via WWW at URL: http://www.cs.umn.edu/~kumar.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Minnesota, Minneapolis, MN, 55455, USA

    Mahesh V. Joshi, Eui-Hong Sam Han, George Karypis & Vipin Kumar

Authors
  1. Mahesh V. Joshi
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  2. Eui-Hong Sam Han
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  3. George Karypis
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  4. Vipin Kumar
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Editor information

Editors and Affiliations

  1. Computer Science Department, Rensselaer Polytechnic Institute, Troy, NY, 12180, USA

    Mohammed J. Zaki

  2. K55/B1, IBM Almaden Research Center, 650 Harry Road, San Jose, CA, 95120, USA

    Ching-Tien Ho

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© 2002 Springer-Verlag Berlin Heidelberg

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Joshi, M.V., Han, EH.S., Karypis, G., Kumar, V. (2002). Efficient Parallel Algorithms for Mining Associations. In: Zaki, M.J., Ho, CT. (eds) Large-Scale Parallel Data Mining. Lecture Notes in Computer Science(), vol 1759. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46502-2_5

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  • DOI: https://doi.org/10.1007/3-540-46502-2_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67194-7

  • Online ISBN: 978-3-540-46502-7

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