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Density-Based Mining of Quantitative Association Rules

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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

Many algorithms have been proposed for mining of boolean association rules. However, very little work has been done in mining quantitative association rules. Although we can transform quantitative attributes into boolean attributes, this approach is not effective and is difficult to scale up for high dimensional case and also may result in many imprecise association rules. Newly designed algorithms for quantitative association rules still are persecuted by nonscalable and noise problem. In this paper, an efficient algorithm, QAR-miner, is proposed. By using the notion of “density” to capture the characteristics of quantitative attributes and an efficient procedure to locate the “dense regions”, QAR-miner not only can solve the problems of previous approaches, but also can scale up well for high dimensional case. Evaluations on QAR-miner have been performed using both synthetic and real databases. Preliminary results show that QAR-miner is effective and can scale up quite linearly with the increasing number of attributes.

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Author information

Authors and Affiliations

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

    David W. Cheung, Lian Wang, S. M. Yiu & Bo Zhou

Authors
  1. David W. Cheung
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  2. Lian Wang
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  3. S. M. Yiu
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  4. Bo Zhou
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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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Cheung, D.W., Wang, L., Yiu, S.M., Zhou, B. (2000). Density-Based Mining of Quantitative Association Rules. 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_32

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  • DOI: https://doi.org/10.1007/3-540-45571-X_32

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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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