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一种改进的频繁集挖掘方法 被引量:10

AN IMPROVED METHOD OF FREQUENT ITEMSETS MINING
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摘要 为了有效解决关联规则挖掘中最关键的一步即频繁集的产生 ,构造了一个新的频繁树结构 ,以存储数据库中频繁项的信息 ,且基于该频繁树给出挖掘频繁集的算法 .该方法能够避免重复扫描数据库 ,避免产生大量的候选集 。 In order to solve the most important step in associat io n rules mining,a new frequent tree structure for storing the crucial information of frequent items in database was proposed,and a new algorithm of mining freque nt itemsets was presented based on the frequent tree.By this method,repeated sca ns of database and yields of large amount of candidate itemsets could be avoided .The search space was also reduced greatly.
出处 《广西师范大学学报(自然科学版)》 CAS 2001年第3期22-26,共5页 Journal of Guangxi Normal University:Natural Science Edition
基金 中科院计算技术研究所智能信息处理开放实验室开放课题 (IIP2 0 0 1 -4 ) 广西自然科学基金资助项目(0 0 0 70 0 8) 广西十百千人才工程资助项目
关键词 频繁集 频繁树 条件项集库 数据挖掘 关联规则 frequent itemset frequent tree conditional itemset bas e data mining association rule
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参考文献7

  • 1Cheung D,Vincent T.Efficient mining of association rules in distributed databases[J].IEEE Transactions on Knowledge and Data Engineering,1996,8(6):911-922.
  • 2Agrawal 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.
  • 3陆丽娜,陈亚萍,魏恒义,杨麦顺.挖掘关联规则中Apriori算法的研究[J].小型微型计算机系统,2000,21(9):940-943. 被引量:140
  • 4Li Shen,Hong Shen,Ling Cheng.New algorithms for efficient mining of association rules[J].Information Sciences,1999,118(4):251-268.
  • 5Bing 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.
  • 6Chen 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.
  • 7马献明,严小卫,陈宏朝.个性化网上信息代理技术的研究概述[J].广西师范大学学报(自然科学版),2000,18(3):40-44. 被引量:19

二级参考文献3

共引文献157

同被引文献54

  • 1伊卫国,卫金茂,王名扬,王兴通.基于数据库划分的高效关联规则挖掘算法研究[J].东北师大学报(自然科学版),2004,36(4):45-50. 被引量:7
  • 2张兵,聂永红,林士敏.NPSP:一种高效的序列模式增量挖掘算法[J].广西师范大学学报(自然科学版),2004,22(4):22-26. 被引量:4
  • 3罗可,贺才望.基于Apriori算法改进的关联规则提取算法[J].计算机与数字工程,2006,34(4):48-51. 被引量:22
  • 4Agrawal 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.
  • 5Wu 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.
  • 6Zhang Chengqi,Zhang Shichao. Association rule mining[M].Berlin:Springer-Verlag,2002.47-83.
  • 7Savasere 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.
  • 8Brin 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.
  • 9Padmanabhan 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.
  • 10Padmanabhan 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.

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