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基于语言值模糊关系的关联规则挖掘算法 被引量:2

Association Rules Mining Algorithm Based on Fuzzy Relation of Linguistic Value
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摘要 针对模糊关联规则挖掘时隶属函数的确定困难以及区间划分边界过硬等问题,提出了模糊关系关联规则挖掘算法,确定了关系等级数目和相邻等级相似度,将语言表达式(事务的属性值)根据模糊运算规则映射到标签集的各个等级上得到等级权值。在这些权值的基础上定义了模糊关系支持度和置信度,阐述了算法的详细步骤,最后给出了算法在服务信任领域挖掘关联规则的应用过程。 For the difficult determining of the membership function and the hard border of interval division when the fuzzy association rules are mining, association rules mining algorithm based on fuzzy relation is proposed. After the relation grade numbers and the similarity of neighbor grades are determined, the linguistic expressions (the properties of transactions) are mapped to each grade of the label set and then get the grade ratings as the weights. Based on these weights, the fuzzy relation support and confidence are defined. The detailed steps of the algorithm are elaborated in this paper. At the end of this article the application of this proposed algorithm about mining association rules in service trust area is described.
出处 《电信科学》 北大核心 2012年第1期113-117,共5页 Telecommunications Science
基金 国家自然科学基金资助项目(No.61003254) 国家科技支撑计划基金资助项目(No.2008BAH24B03) 浙江省自然科学基金资助项目(No.Y1080130 No.Y1101304)
关键词 语言值 模糊关系 关联规则 服务信任领域 linguistic value, fuzzy relation, association rule, service trust area
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  • 1史忠植.高级人工智能[M].北京:科学出版社,1997.60-100.
  • 2Agrawal R, Imieliski T, Swami A. Mining association rules between sets of items in large database[A]. Proc of ACM SIGMOD Intelrnational Conference on Management of Data (SIGMOD'93)[C].1 993. 207 - 216.
  • 3Agrawal R, Srikant R, Vu Q. Mining association rules with item constraints [A]. The Third International Conference on Knowledge Discovery inDatabases and Data Mining[C]. California, August 1997. 327-331.

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  • 1Chien Y W C,Chen Y L.Mining associative classification rules with stock trading data-A GA-based method[J].Knowledge-Based Systems,2010,23(6):605-614.
  • 2Mc Auley A,Sinkar K,Kant L,et al.Tuning of reinforcement learning parameters applied to OLSR using a cognitive network design tool[C]//Wireless Communications and Networking Conference(WCNC),2012,2786-2791.
  • 3Li W,Han J,Pei J.CMAR:Accurate and efficient classification based on multiple class-association rules[C]//1st IEEE international conference on data mining San Jose,CA,USA,2009,43(5):233-241.
  • 4Hong T P,Lin C W,Wu Y L.Maintenance of fast updated frequent pattern trees for record deletion[J].Computational Statistics and Data Analysis,2009,53(7):485-494.
  • 5Hong T P,Wang C J.An efficient and effective association-rule maintenance algorithm for record modification[J].Expert Systems with Applications,2010,37(1):618-626.
  • 6Vo B,Le B.A novel classification algorithm based on association rules mining[M]//Knowledge Acquisition:Approaches,Algorithms and Applications.Springer Berlin Heidelberg,2009:61-75.
  • 7Hong T P,Wang C Y,Tseng S S.An incremental mining algorithm for maintaining sequential patterns using pre-large sequences[J].Expert Systems with Applications,2011,38(6):751-758.
  • 8Lim A H L,Lee C S.Processing online analytics with classification and association rule mining[J].Knowledge-Based Systems,2010,23(3):248-255.
  • 9刘步中.基于频繁项集挖掘算法的改进与研究[J].计算机应用研究,2012,29(2):475-477. 被引量:31
  • 10邵保胜,孟志青,蒋敏.一种基于时态因子约束的关联规则挖掘算法[J].浙江工业大学学报,2012,40(1):60-64. 被引量:4

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