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关联规则评价方法综述 被引量:5

Survey on Methods of Evaluation for the Association Rules
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摘要 关联规则的评价方法可分为客观评价和主观评价两种,大部分都是基于统计学的方法。本文对各种关联规则评价方法进行了综述,并阐述了各自的优缺点。 Methods of evaluation for the Association Rules can be divided into two groups: objective evaluation and subjective evaluation. Many of these methods are based upon statistics. In this paper, Various methods of evaluation for the Association Rule are summarized and merits and demerits of each method are expounded.
出处 《安徽科技学院学报》 2007年第5期37-40,共4页 Journal of Anhui Science and Technology University
关键词 关联规则 评价 客观评价 主观评价 Association Rules Evaluation Objective evaluation Subjective evaluation
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  • 1[1]Srikant R, Agrawal R. Mining quantitative association rules in large relation table[C]. In: Proceeding of the ACMSIGMOD Conference on Management of Data, Montreal, Canada, 1996. 1~12.
  • 2[2]Jiawei Han, Micheline Kamber. Data mining: concept and techniques[M]. Academic Press.2000.
  • 3[3]Fukuda T, Morimoto Y, Morishita S. Data mining using two-dimensional optimized association rules: scheme, algorithms and visualization[C]. In: Proceeding of the ACMSIGMOD Conference on Management of Data, Montreal, Canada, 1996. 13~24.
  • 4[4]Miller R, Yang Y. Association rules over interval data[C]. In: Proceeding of the ACMSIGMOD Conference on Management of Data, Tucson, USA, 1997. 1~10.
  • 5[4]KLEMETIMEN M,MANNILA H,et al.Finding interesting rules from large sets of discovered association rules[A].CIKM′94[C].ACM Press,1994.401-407.
  • 6[5]PARK JS,et al. Using a hash-based method with transaction trimming for mining association rules[A].IEEE Trans on Knowledge and Data Engineering[C].1997,9(5):813-825.
  • 7Agrawal, 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.
  • 8Piatesket-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.
  • 9Symth, 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.
  • 10Toivonen, 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.

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