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A Concurrent Approach to the Key-Preserving Attribute-Oriented Induction Method

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Knowledge Discovery and Data Mining. Current Issues and New Applications (PAKDD 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

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Abstract

Attribute-Oriented Induction (AOI) reduces the search space of large data to produce a minimal rule set. Classical AOI techniques only consider attributes that can be generalised but eliminates keys to relations. The Key-Preserving AOI (AOI-KP) preserves keys of the input relation and relate them to the rules for subsequent data queries. Previously, the sequential nature of AOI-KP affected performance on a single processor machine. More significantly, time was spent doing I/O to files linked to each generated rule. AOI-KP is O (np) and storage requirement O (n), where n and p represent the number of input and generalised tuples respectively. We present two enhanced AOI-KP algorithms, concAOI-KP (concurrent AOI-KP) and onLineConcAOI-KP of orders O (np) and O (n) respectively. The two algorithms have storage requirement O (p) and O (q), q =p*r, 0<r<l respectively. A prototype support tool exists and initial results indicate substantially increased utilisation of a single processor.

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References

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Authors and Affiliations

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

    Maybin K. Muyeba & John A. Keane

Authors
  1. Maybin K. Muyeba
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  2. John A. Keane
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Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

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© 2000 Springer-Verlag Berlin Heidelberg

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Muyeba, M.K., Keane, J.A. (2000). A Concurrent Approach to the Key-Preserving Attribute-Oriented Induction Method. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_36

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  • DOI: https://doi.org/10.1007/3-540-45571-X_36

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

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