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关联规则的快速更新算法 被引量:1

Fast Update Algorithm for Association Rule
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摘要 针对基于支持度变化的最大频繁项集维护问题、频繁项集与最大频繁项集互转换时的维护问题,提出3种相应算法。在挖掘最大频繁项集的过程中不断调节支持度大小,以实现其快速更新。基于最大频繁项集子集的支持计数,将现有最大频繁项集转换为频繁项集。 Aiming at the maintenance problems of maximum frequent itemsets based on support change and interconversion between frequent itemsets and maximum frequent itemsets, this paper proposes three relevant algorithms. It adjusts the support rating constantly during the process of mining maximum frequent itemsets to realize fast update of maximum frequent itemsets. Based on support counting of the subsets of maximum frequent itemsets, existing maximum frequent itemsets are transformed into frequent itemsets.
出处 《计算机工程》 CAS CSCD 北大核心 2009年第19期62-64,68,共4页 Computer Engineering
基金 辽宁省教育厅青年基金资助项目(20040052)
关键词 最大频繁项集 数据挖掘 更新 频繁模式树 maximum frequent itemsets data mining update frequent pattern tree
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  • 1Han J.W.,Kamber M..Data Mining:Concepts and Techniques.Beijing:Higher Education Press,2001.
  • 2Agrawal R.,ImielinSki T.,Swami A..Mining association rules between sets of items in large database.In:Proceedings of the ACM SIGMOD International Conference on Managementof Data,Washington,DC,1993,2:207-216.
  • 3Srikant A.R..Fast algorithms for mining association rules.In:Proceedings of the 20th International Conference Very Large Data Bases(VLDB’94).Santiago,Chile,1994,487-499.
  • 4Han J.W.,Pei J.,Yin Y..Mining partial periodicity using frequent pattern tree.Simon Fraser University:Technical Report TR-99-10,1999.
  • 5Cheung D.,Han J.W.,Ng V.,Wong V..Maintenance of discovered association rules in large databases:An incremental updating technique.In:Proceedings of the 12th International Conference on Data Engineering(ICDE),New Orleans,Louisiana.1996.106-114.
  • 6Cheung D.LEE S.Kao B.A general incremental technique for maintaining discovered association rules.In:Proceedings of the 5th International Conference on Database Systems for Advanced Applications(DASFAA),Melbourne,Australia,World Scientific,1997,185-194.
  • 7Han J.W.,Pei J.,Yin Y..Mining frequent patterns without candidate generation.In:Proceedings of the 2000 ACM-SIG-MOD International Conference on Management of Data,Dal1as,2000,1-12.
  • 8Bayardo R.J..Efficiently mining long patterns from databases.In:Haas L.M.,Tiwary A.eds..Proceedings of the ACMSIGMOD International Conference on Management of Data.New York:ACM Press,1998,85-93.
  • 9Lin D.,Kedem Z.M..Pincer-Search:A new algorithm for discovering the maximum frequent set.In:Scheck H.J.,Saltot F.,Ramos I.et al,eds..Proceedings of the 6th European Conference on Extending Database Technology,Heidelberg:Snringer-Verlag.199R.105-119.
  • 10Lin Dao I,Proc the 6th European Conference on Extending Database Technology,1998年,105页

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同被引文献15

  • 1Agrawal R, Imieliski T, Swami A. Mining association rulesbetween sets of items in large databases [ C ] // Proceedingsof the 1993 ACM SIGMOD International Conference onManagement Data. 1993,22(2) :207-216.
  • 2Savasere A, Omiecinski E,Navathe S. An efficient algo-rithm for mining association rules in large databases[ C]//Proceedings of the 21st VLDB Conference. 1995.
  • 3Toivonen H. Sampling large databases for association rules[C]// Proceedings of the 22th VLDB Conference. 1996 :134-145.
  • 4Park J S,Chen M S,Yu P S. An effective hash based al-gorithm for mining association rules [ C]// Proceedings ofthe 1995 ACM SIGMOD International Conference on Man-agement of data. 1995,24(2) :175-186.
  • 5Agrawal R, Srikant R. Fast algorithm for mining associationrules[ C]// Proceedings of the 20th International Conferenceon Very Large Data Bases (VLDB). 1994:487499.
  • 6Han Jiawei, Pei Jian, Yin Yiwen. Mining frequent patternswithout candidate generation [ C ]// Proceedings of the2000 ACM SIGMOD International Conference on Manage-ment of Data. 2000,29(2) : 1-12.
  • 7刘华婷,郭仁祥,姜浩.关联规则挖掘Apriori算法的研究与改进[J].计算机应用与软件,2009,26(1):146-149. 被引量:119
  • 8杨柯,张建军.基于计算期望和信誉度的网格资源调度模型[J].西北大学学报(自然科学版),2009,39(2):225-229. 被引量:4
  • 9王爱平,王占凤,陶嗣干,燕飞飞.数据挖掘中常用关联规则挖掘算法[J].计算机技术与发展,2010,20(4):105-108. 被引量:69
  • 10郭秀娟,张树彬,岳俊华.基于Apriori数据挖掘算法研究[J].吉林建筑工程学院学报,2010,27(3):57-60. 被引量:8

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