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一种新的变化挖掘显露模式及其挖掘算法 被引量:1

A New Kind of Emerging Pattern of Change Mining and Its Mining Algorithm
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摘要 针对实际应用中用户对关联规则的多个兴趣度指标变化感兴趣的问题,提出了一种新的变化挖掘显露模式,不仅考虑了支持度的变化,而且考虑了置信度或其它兴趣度指标的变化.基于Pareto排序,还设计了相应的显露模式的挖掘算法.实证分析的结果表明,所提算法可以有效地识别2个时期的数据集显露模式. According to the fact that users are interested in the changes of multiple interest measures of association rules, a new kind of emerging pattern is presented which considers not only the changes of support but also the changes of confi- dence and other measures. Based on the Pareto sorting, a corresponding mining algorithm for Emerging Pattern is designed. Empirical evaluation shows that the proposed algorithm is very effective to recognize emerging patterns from two-period datasets.
出处 《信息与控制》 CSCD 北大核心 2013年第3期308-313,共6页 Information and Control
基金 国家自然科学基金资助项目(60804047 71273139)
关键词 变化分析 变化挖掘 关联规则 显露模式 Pareto排序 change analysis change mining association rule emerging pattern Pareto sorting
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参考文献13

  • 1Williams G J, Christen R Visualizing temporal cluster changes using relative density self-organizing maps[J]. Knowledge and Information Systems, 2010, 25(2): 281-302.
  • 2Kim J K, Song H S. Kim T S, et al. Detecting the change of cus- tomer behavior based on decision tree analysis[J]. Expert Sys- tems, 2005, 22(4): 193-205.
  • 3Shih M J, Liu D R, Hsu M L. Discovering competitive intel- ligence by mining changes in patent trends[J]. Expert Systems with Applications, 2010, 37(4): 2882-2890.
  • 4Song H S, Kim J K, Kim S H. Mining the change of customer behavior in an intemet shopping mall[J]. Expert Systems with Applications, 2001, 21(3): 157-168.
  • 5Chen M C, Chiu A L, Chang H H. Mining changes in customer behavior in retail marketing[J]. Expert Systems with Applica- tions, 2005, 28(4): 773-781.
  • 6Liu D R, Shih M J, Liau C J, et al. Mining the change of event trends for decision support in environmental scanning[J]. Expert Systems with Applications, 2009, 36(2): 972-984.
  • 7Madani S. Mining changes in customer purchasing behavior[D]. Sweden: LuleaUniversity of Technology, 2009.
  • 8Bala P K. A distance-based approach for mining changes in pur- chase behavior in retail sale[J]. International Journal of Com- puter Applications, 2010, 1 (5): 60-64.
  • 9段磊,唐常杰,Guozhu Dong,杨宁,苟驰.基于显露模式的对比挖掘研究及应用进展[J].计算机应用,2012,32(2):304-308. 被引量:8
  • 10Dong G Z, Li J Y. Efficient mining of emerging patterns: Dis- covering trends and differences[C]//Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Dis- covery and Data Mining. USA: AAAI Press, 1999: 43-52.

二级参考文献38

  • 1许洪涛 范明 昝红英.一种基于EP的中文文本自动分类算法.计算机研究与发展,2005,42(1):351-355.
  • 2AGRAWAL R,SRIKANT R.Fast algorithms for mining association rules[C]//Proceedings of the 20th International Conference on Very Large Data Bases.San Francisco:Morgan Kaufmann,1994:487-499.
  • 3BAY D S,PAZZANI M J.Detecting change in categorical data:mining contrast sets[C]// Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,1999:302-306.
  • 4WANG HAIXUN,FAN WEI,YU P S,et al.Mining concept-drifting data streams using ensemble classifiers[C]//Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2003:226-235.
  • 5DONG GUOZHU,LI JINYAN.Efficient Mining of emerging patterns:discovering trends and differences[C]// Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,1999:43-52.
  • 6BAILEY J,MANOUKIAN T,RAMAMOHANARAO K.Fast algorithms for mining emerging patterns[C]// Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery.London:Springer-Verlag,2002:39-50.
  • 7FAN HONGJIAN,RAMAMOHANARAO K.An efficient single-scan algorithm for mining essential jumping emerging patterns for classification[C]// Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining.London:SpringerVerlag,2002:456-462.
  • 8LI JINYAN,LIU GUIMEI,WONG L.Mining statistically important equivalence classes and delta-discriminative emerging patterns[C]//Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2007:430-439.
  • 9BAILEY J,MANOUKIAN T,RAMAMOHANARAO K.A fast algorithm for computing hypergraph transversals and its application in mining emerging patterns[C]//Proceedings of the Third IEEE International Conference on Data Mining.Washington,DC:IEEE Computer Society,2003:485-488.
  • 10MINATO S.Zero-suppressed BDDs for set manipulation in combinatorial problems[C]// Proceedings of the 30th International Design Automation Conference.New York:ACM,1993:272-277.

共引文献7

同被引文献6

  • 1范明,刘孟旭,赵红领.一种基于基本显露模式的分类算法[J].计算机科学,2004,31(11):211-214. 被引量:11
  • 2HAN J,MICHELINE K.数据挖掘概念与技术[M].北京:机械工业出版社,2004.
  • 3GUOZHU DONG, XIUZHEN ZHANG. CAEP: classification by aggregation emerging patterns [J]. Discovery Science , 1999 (5) : 265-368..
  • 4ZHOU JIN,JIUYONG LI. Discovery of causal rules using partial association[C]. 2012 IEEE 12th International Conferenee on Data Mining, 2012: 309-318.
  • 5JIUYONG LI, THUC DUY LE, LIN LIU,et al. Mining causal as- sociation rules [C]. 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 : 114-123.
  • 6段磊,唐常杰,Guozhu Dong,杨宁,苟驰.基于显露模式的对比挖掘研究及应用进展[J].计算机应用,2012,32(2):304-308. 被引量:8

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