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利用项集有序特性改进Apriori算法 被引量:11

AN IMPROVED APRIORI ALGORITHM BY REORDERING ITEMSETS
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摘要 Apriori算法是挖掘关联规则的一个经典算法,通过分析、研究该算法的基本思想,并利用项集的有序特性对其进行改进,减少了生成的候选集数量,从而提高算法的效率. The Apriori algorithm is a classical algorithm for mining association rules.In this paper,we deeply study the idea of the Apriori algorithm,and present an improved algorithm by reordering itemsets,called ImpApri.The number of candidate itemsets can be largely reduced,and its efficiency is higher than that of the original Apriori algorithm.
出处 《广西师范大学学报(自然科学版)》 CAS 2004年第1期33-37,共5页 Journal of Guangxi Normal University:Natural Science Edition
基金 澳大利亚国家大型项目(ARC:DP0343109)
关键词 APRIORI算法 挖掘关联规则 非频繁项集 有序特性 数据挖掘 Apriori algorithm mining association rules frequent itemsets infrequent itemsets
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参考文献8

  • 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
  • 2李绪成,王保保.挖掘关联规则中Apriori算法的一种改进[J].计算机工程,2002,28(7):104-105. 被引量:71
  • 3Agrawal R ,Imielinski T ,Swami A. Mining association rules between sets of items in large databases[A]. Proceedings of the ACM SIGMOD international conference on management of data[C]. New York:ACM Press, 1993. 207-216.
  • 4Jong Soo Park,Ming-Syan Chen ,Philip S Yu. Using a hash based method with transaction trimming for mining association rules [J]. IEEE Transactions on Knowledge and Data Engineering, 1997,9(5):813-825.
  • 5Agrawal R,Srikant R. Fast algorithms for mining association rules[A]. Proceedings of the 20th VLDB conference[C].San Mateo:Morgan Kaufmann Publishers,1994.487-499.
  • 6Han J,Pei J,Yin Y. Mining frequent patterns without candidate generation[A]. Proceedings of the 2000 ACM SIG-MOD international conference on management of data[C]. New York:ACM Press,2000.1-12.
  • 7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
  • 8张师超,张成奇.多数据库挖掘的研究[J].广西师范大学学报(自然科学版),2003,21(1):153-153. 被引量:5

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.

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