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一种改进的负关联规则挖掘算法 被引量：8

AN IMPROVED ALGORITHM FOR IDENTIFYING NEGATIVE ASSOCIATION RULES
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摘要 负关联规则A→ B(或者 A→B, A→ B)描述的是项目之间的互斥关系,其与传统的关联规则有着同样重要的作用.然而,负关联规则和传统正关联规则的挖掘有很大不同,因为负关联规则隐藏在数量巨大的非频繁项集中.因此提出一种新的挖掘horn子句类型负关联规则的算法,并且实验证明是行之有效的. Negative association rules (NAR) catch mutually-exclusive correlations among items.They play important roles just as traditional association rules (TAR) do.For example,in stock market surveillance based on alert-logs,NARs detect which alerts are false.There are essential differences between mining TARs and NARs because NARs are hidden in infrequent itemsets.This paper presents a new algorithm for mining horn-clause-based negative association rules.To evaluate this algorithm,the authors have illustrated the efficiency by a group of experiments.
作者 卢景丽 徐章艳 刘美玲 区玉明
机构地区 广西师范大学数学与计算机科学学院
出处 《广西师范大学学报（自然科学版）》 CAS 2004年第2期41-46,共6页 Journal of Guangxi Normal University:Natural Science Edition
基金 澳大利亚ARC基金资助项目(DP0343109)
关键词 数据挖掘 关联规则 负关联规则 兴趣度 负项集 data mining association rules negative association rules interestingness negative itemsets
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献11

  • 1Agrawal R,Imielinski T,Swami A. Mining association rules between sets of items in large database [A]. Proceedings of the ACM SIGMOD conference on management of data[C]. New York:ACM Press, 1993. 207-216.
  • 2Wu Xindong,Zhang Chengqi,Zhang Shichao. Mining both positive and negative association rules[A]. Proceedings of the 19th international conference on machine learning [C]. San Mateo:Morgan Kaufmann Publishers, 2002. 658-665.
  • 3Zhang Chengqi,Zhang Shichao. Association rule mining[M].Berlin:Springer-Verlag,2002.47-83.
  • 4Savasere A,Omiecinski E,Navathe S. Mining for strong negative associations in a large database of customer transactions[A]. Proceedings of the international conference on data engineering[C]. 1998. 494-502.
  • 5Brin S,Motwani R,Silverstein C. Beyond market baskets:generalizing association rules to correlations[A]. Proceedings of the ACM SIGMOD international conference on management of data[C]. New York :ACM Press, 1997. 265-276.
  • 6Padmanabhan B,Tuzhilin A. Small is beautiful :discovering the minimal set of unexpected patterns[A]. Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining[C]. New York :ACM Press,2000.54-63.
  • 7Padmanabhan B,Tuzhilin A. A belief-driven method for discovering unexpected patterns[A]. Proceedings of the 4th international conference on knowledge discovery and data mining[C]. New York :AAAI Press, 1998.94-100.
  • 8Hwang S,Ho S,Tang J. Mining exception instances to facilitate workflow exception handling[A]. Proceedings of the 6th international conference on database systems for advanced applications [C]. Los Alamitos, CA: IEEE Computer Society Press, 1999.45-52.
  • 9Hussain F,Liu H,Suzuki E,et al. Exception rule mining with a relative interestiness measure[A]. Proceedings of the 4th Pacific-Asia conference on knowledge discovery and data mining[C]. Berlin :Springer-Verlag,2000.86-97.
  • 10唐懿芳,牛力,张师超.多数据源关联规则挖掘算法研究[J].广西师范大学学报（自然科学版）,2002,20(4):27-31. 被引量：14

二级参考文献12

  • 1苏毅娟,严小卫.一种改进的频繁集挖掘方法[J].广西师范大学学报（自然科学版）,2001,19(3):22-26. 被引量：10
  • 2Agrawal R,Imielinski T,Swami A. Mining associations between sets of items in large databases[A]. Proceeding of the 1993 ACM-SIGMOD international conference on management of data[C]. Washington:Springer-Verlag,, 1993.207-216.
  • 3Shichao Zhang. Aggregation and maintenance for databases mining,intelligent data analysis:an international journal[J]. Elesvia, 1999,3 (6): 475- 490.
  • 4Zhong N,Yao Y,Ohsuga S. Peculiarity oritented multi-database mining[A]]. Proceedings of PKDD'99[C]. Washington: Springer-Verlag, 1999.251- 254.
  • 5唐懿芳 牛力 严小卫 等.数据挖掘存在问题的探讨[J].计算机应用研究,2002,:60-62.
  • 6Cheung D,Vincent T.Efficient mining of association rules in distributed databases[J].IEEE Transactions on Knowledge and Data Engineering,1996,8(6):911-922.
  • 7Agrawal R,Imielinski T,Swamy A.Mining association rules between sets of items in large databases[A].Proceedings of ACM SIGMOD International conference on Management of Data[C].Washington:Springer-Verlag,1993.458-466.
  • 8Li Shen,Hong Shen,Ling Cheng.New algorithms for efficient mining of association rules[J].Information Sciences,1999,118(4):251-268.
  • 9Bing Liu,Wynne Hsu,Lai-Fun Mun,Hing-Yan Lee.Finding interesting patterns using user expections[J].IEEE Transactions on Knowledge and Data Engineering,1999,11(6):817-832.
  • 10Chen M,Han J,Yu P S.Data Mining:An overview from database perspective[J].IEEE Transactions on Knowledge and Data Engineering,1996,8(6):866-883.

共引文献20

同被引文献56

引证文献8

二级引证文献32

广西师范大学学报（自然科学版）
2004年 第2期

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