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KDD中因果关联规则的评价方法
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    Abstract:

    It is very important to evaluate the discovered rules in KDD (knowledge discovery in database). An evaluation method for causal rules is provided in this paper. The new and valid knowledge expression (language field and language value) and the reasoning mechanism (qualitative induction mechanism of causal relation) are used. The method is general and interactive. Its construction and the algorithm are given, and its validity is proved through case. By the comparison with the related work, it is proved to be an advanced method.

    Reference
    [1] Agrawal, R., Mannila, H., Srikant, R., et al. Fast discovery of association rules. In: Fayyad, M., Piatetsky-Shapiro, G., Smyth, P., eds. Advances in Knowledge Discovery and Data Mining. Menlo Park, CA: AAAI/MIT Press, 1996. 307～328.
    [2] Piatesket-Shapiro, G. Discovery, analysis, and presentation of strong rules. In: Piatesky-Shapiro, G., Frawley, W.J., eds. Advances in Knowledge Discoveryand Data Mining. Menlo Park, CA: AAAI/MIT Press, 1991. 229～238.
    [3] Symth, P., Goodman, R.M. An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering, 1992,4(4):301～316.
    [4] Toivonen, H., Klemettinen, M., Ronkainen, P., et al. Pruning and grouping discovered association rules. In: Mlnet Workshop on Statistics, Machine Learning, and Discovery in Database. 1995. 47～52. http://citeseer.nj.nec.com/toivonen95pruning.html.
    [5] Yang Bing-ru. FIA and CASE based on fuzzy language field. Fuzzy Sets and Systems, 1998,95(2):83～89.
    [6] Yang, Bing-ru. A valid mehod to judge FUZZY causal relation. In: The Selection of Mathematic Production. Tianjin: Tianjin Science and Technology Publishing House, 1983. 137～147 (in Chinese).
    [7] Piatetsky-Shapiro, G., Matheus, C.J. The interestingness of deviations. In: Proceedings of the AAAI'94, Workshop on Knowledge Discovery in Databases. 1994. 25～36. http://citeseer.nj.nec.com/piatetsky-shapiro94interestingness.html.
    [8] Silberschatz, A., Tuzhilin, A. What makes patterns interesting in knowledge discovery system. IEEE Transactions on Knowledge and Data Engineering, 1996,8(6):970～974.
    [9] Liu, Bing, Hsu, Wynne, Mun, Lai-Fun, et al. Finding interesting patterns using user expectations. IEEE Transactions on Knowledge and Data Engineering, 1999,11(6):817～832.
    [10] 杨炳儒.FUZZY因果联系的一种能行可判定方法.天津市数学研究成果选编.天津:天津科技出版社,1983.137～147.
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杨炳儒,綦艳霞. KDD中因果关联规则的评价方法.软件学报,2002,13(6):1142-1147

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History
  • Received:February 15,2001
  • Revised:June 05,2001
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