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基于模糊概念的量化关联规则挖掘 被引量：3

Mining Quantitative Association Rules Based on Fuzzy Concepts
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摘要 提出了一种新的基于模糊概念的量化关联规则挖掘方法。该方法利用在量化属性域上定义的一组模糊概念表示属性间的关联关系，克服了传统的离散分区法的不足，使得规则的表示自然、简明，有利于专家理解。同时，给出了挖掘算法。 A novel method for mining quantitative association rules based on fuzzy concepts is proposed. It employs a set of fuzzy concepts, which are defined in quantitative attribute domains, to represent the revealed regularities among quantitative attributes and effectively overcomes the drawbacks caused by the traditional discrete interval method. Besides, an algorithm for mining quantitative association rules is presented.
作者 李乃乾 沈钧毅
机构地区 西安交通大学计算机软件研究所
出处 《计算机工程》 CAS CSCD 北大核心 2002年第11期13-14,22,共3页 Computer Engineering
基金 国家自然科学基金项目(60173058)
关键词 模糊概念 量化 关联规则 知识发现 数据挖掘 数据库 Knowledge discovery Data mining Association rules Fuzzy association rules
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
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参考文献5

  • 1Agrawal R, Imielinski T, Swami A. Mining Association Rules Between Set of Items in Large Databases. In: Proceedings of the 1993 ACMSIGMOD Conference on Management of Data, Washington,DC.,1993:207 -216
  • 2Han J, Kambr M. Data Mining: Concepts and Techniques. Beijing: Higher Education Press, 2001:229-230
  • 3Agrawal R, Strikant R. Fast Algorithms for Mining Association Rules. In: Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994:247-299
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  • 5Srikant R, Agrawal R. Mining Quantitative Association Rules in Large Relational Tables. In: Proceedings of the ACM SIGMOND Internatonal Confernce on Management of Data, Monreal, Canada, 1996:1 - 12

同被引文献27

  • 1张德丰,马子龙,梁忠宏.基于聚类和关联规则的挖掘算法[J].计算机工程与科学,2004,26(9):64-66. 被引量：8
  • 2亢海力,王来生,蔡永旺.基于概率的模糊加权关联规则挖掘[J].计算机应用,2006,26(B06):113-114. 被引量：6
  • 3李凡长.基于动态模糊产生式系统的知识表示方法研究[J].计算机科学,1997,24:3234-3234.
  • 4Srikant R, Agrawal R. Mining quantitative association rules in large relational tables [ C]//Proceedings of the 1996 ACMSIGMOD International Conference on Management of Data. Canada, Morzreal, 1996 : 1 - 12.
  • 5Chan M K, Fu A, Man H W. Mining fuzzy association rules in database[ C] // Proceedings of the ACM Sixth International Conf on Information and Knowledge Management. Las Vegas, Neveda, 1997:10- 14.
  • 6Stolfo S J, Prodromidis A L. Tselepis S, Lee W, Fan DW, Chart PK. JAM : Java agents for meta-learning over distributed databases [ J ]. International KDD' 97 Conference. 1997,24( 3 ) :74 - 81.
  • 7Chattratichat J, Darlington J, Guo Y, Hedvall S, Koler M, Syed J. An architecture for distributed enterprise data mining [ J ]. HPCN Europe 1999, Lecture Notes in Computer Science. 1999,30 (2) :573 - 582.
  • 8A Weiss. Computing in the Clounds [ J ]. NetWorker. Dec. 2007,12 (4) : 16 - 25.
  • 9Luo, J. Integrating Fuzzy Detection : [ MS Thesis ] [D]. Logic with Data Mining Mississippi State University, Methods for Intrusion [ J ]. 2008,30 (3) :264 -265.
  • 10Kuok, C, A. Fu, M. Wong. Mining fuzzy association rules in databases [ J ] . SIGMOD Record,2009,24 ( 1 ) :41 - 6.

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计算机工程
2002年 第11期

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