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Association Rules

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

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Abstract

New association rules are presented for measure of association relationships between patterns. The new association rules are shown to not only measure three well-known association relationships correctly, but also satisfy other criteria for correct measure of association. Comparison with other measures is discussed both theoretically and experimentally. Applications in supervised mining of association rules and in pattern-driven multidimensional pattern analysis are presented.

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References

  • R. Agrawal, T. Imielinski, and A. Swami. 1993a. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on the Management of Data, pages 207–216.

    Google Scholar 

  • R. Agrawal, T. Imielinski, and A. Swami. 1993b. Database mining: a performance perspective. IEEE Transactions on Knowledge and Data Engineering, 5:914–925.

    Article  Google Scholar 

  • R. Agrawal and R. Srikant. 1994. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, pages 487–499.

    Google Scholar 

  • R. Agrawal, A. Arning, T. Bollinger, M. Mehta, J. Shafer, and R. Srikant. 1996a. The Quest Data Mining System. In Proceedings of the Second International Conference on Knowledge Discovery in Databases and Data.

    Google Scholar 

  • R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. 1996b. Fast Discovery of Association Rules. In, Fayyad et al G. Piatetsky-Shapiro, P. Smyth, and R. Uthrusamy. 1996. Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park CA (Fayyad et al., 1996), pages 307–328.

    Google Scholar 

  • J. M. Bugajski and J. T. Russo. 1993. US patent No. 5,245,337.

    Google Scholar 

  • J. M. Bugajski and J. T. Russo. 1994. US patent No. 5,293,164.

    Google Scholar 

  • J. M. Bugajski. 1997. US patent No. 5,592,667.

    Google Scholar 

  • A. W. Cheung, J. Han, V. T. Ng, A. Fu, and Y. Fu. 1996. A fast distributed algorithm for mining association rules. In Proceedings of the Fourth International Conference on Parallel and Distributed Information Systems.

    Google Scholar 

  • U. M. Fayyad. G. Piatetsky-Shapiro, P. Smyth, and R. Uthrusamy. 1996. Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park CA.

    Google Scholar 

  • J. Han and Y. Fu. 1995. Discovery of multiple-level association rules from large databases. In Proceedings of the 21th International Conference on Very Large Data Bases, pages 420–431.

    Google Scholar 

  • M. Houtsman and A. Swami. 1995. Set-oriented mining of association rules. In Proceedings of the International Conference on Data Engineering, pages 25–34.

    Google Scholar 

  • M. Klemettine, H. Mannila, P. Ronkainen, H. Toivonen, and A. I. Verkamo. 1994. Finding interesting rules from large sets of discovered association rules. In Proceedings of the 3th International Conference on Information and Knowledge Management, pages 401–407.

    Google Scholar 

  • S. D. Lee, D. W. Cheung, and B. Kao. 1998. Is sampling useful in data mining? A case in the maintenance of discovered association rules. Data Mining and Knowledge Discovery, Vol. 2, number 3, pages 233–262.

    Article  Google Scholar 

  • H. Mannila, H. Toivonen, and A. I. Verkamo. 1994. Efficient algorithms for discovering association rules. In Proceedings of the AAAI Workshop on Knowledge Discovery in Databases, pages 144–155.

    Google Scholar 

  • H. Mannila. 1998. Database methods for data mining. Tutorial for 4th International Conference on Knowledge Discovery and Data Mining.

    Google Scholar 

  • R. Meo, G. Psaila, and S. Ceri. 1998. An extension to SQL for mining association rules. Data Mining and Knowledge Discovery, Vol. 2, number 2, pages 195–224.

    Article  Google Scholar 

  • J. S. Park, M. S. Chen, and P. S. Yu. 1995. An effective hash based algorithm for mining association rules. In Proceedings of the ACM SIGMOD International Conference on the Management of Data, pages 175–186.

    Google Scholar 

  • A. Savasere, E. Omiecinski, and S. Navathe. 1995. An efficient algorithm for mining association rules in large databases. In Proceedings of the 21th International Conference on Very Large Data Bases, pages 432–444.

    Google Scholar 

  • C. Silverstein, S. Brin, and R. Motwani. 1998. Beyond market basket: Generalizing association rules to dependence rules. Data Mining and Knowledge Discovery, Vol. 2, number 1, pages 39–68.

    Article  Google Scholar 

  • R. Srikant and R. Agrawal. 1995. Mining generalized association rules. In Proceedings of the 21th International Conference on Very Large Data Bases, pages 407–419.

    Google Scholar 

  • H. Toivonen. 1996. Sampling large databases for finding association rules. In Proceedings of the 22th International Conference on Very Large Data Bases, pages 134–145.

    Google Scholar 

  • J. Wijsen and R. Meersman. 1998. On the complexity of mining quantitative association rules. Data Mining and Knowledge Discovery, Vol. 2, number 3, pages 263–282.

    Article  Google Scholar 

  • T. Zhang, J. M. Bugajski, and K. R. Raghavan. 1999. US patent No. 5,966,709. T. Zhang. 1999. US patent No. 5,983,232.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Triada Ltd., 323B Vintage Park Dr., Foster City, CA, 94404

    Tao Zhang

Authors
  1. Tao Zhang
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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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Zhang, T. (2000). 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_31

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  • DOI: https://doi.org/10.1007/3-540-45571-X_31

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

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