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基于频繁链表的频繁集的挖掘算法 被引量:5

An Algorithm of Mining Frequent Set Based on Frequent Link
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摘要 The problem of mining frequent set is a key issue in data mining. In this paper, a new method of miningfrequent set based on the frequent link is proposed. The algorithm constructs alternate frequent link from the transac-tion, the alternate link is yielded by adding up the alternate frequent link which constructed by scanning the transac-tion database in proper order. The frequent link that comprises all the information is constructed with the frequentnode which is selected according requirement. Our algorithm need to scan the transaction database only once and easysupervises the change of frequent set in order to guarantee the right of association rule. The problem of mining frequent set is a key issue in data mining. In this paper, a new method of mining frequent set based on the frequent link is proposed. The algorithm constructs alternate frequent link from the transaction, the alternate link is yielded by adding up the alternate frequent link which constructed by scanning the transaction database in proper order. The frequent link that comprises all the information is constructed with the frequent node which is selected according requirement. Our algorithm need to scan the transaction database only once and easy supervises the change of frequent set in order to guarantee the right of association rule.
出处 《计算机科学》 CSCD 北大核心 2003年第7期165-166,共2页 Computer Science
关键词 数据库 频繁集 数据挖掘算法 频繁链表 事物数据库 FL-Generation算法 Frequent link, Frequent set, Transaction database, Data mining
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参考文献6

  • 1路松峰,卢正鼎.快速开采最大频繁项目集[J].软件学报,2001,12(2):293-297. 被引量:113
  • 2王晓峰,王天然.基于双空间搜索的频繁项挖掘方法[J].计算机科学,2002,29(4):55-60. 被引量:7
  • 3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
  • 4Lin, Dao-I,Kedem Z M. Pincer-Search: a new algorithm for discovering the maximun frequent set. In : Schek. H. J. , Saltor, F. ,Ramos,I. ,et al,eds. Proc. of the 6th European Conf. on Extending Database Technology. Heidelberg: Springer-verlag, 1998. 105-119.
  • 5Fayyad U, Stolorz P. Data mining and KDD: Promise and challenges. Future Generation Computer Systems, 1997,13 : 99- 115.
  • 6Shen Li, Shen Hong, Cheng Ling. New algorithms for efficient mining of association rules [J]. Information Sciences, 1999, 118(4) :251-268.

二级参考文献10

  • 1Lin Dao I,Proc the 6th European Conference on Extending Database Technology,1998年,105页
  • 2Agrawal R,Proc the 11th Inter Conference on Data Engineering,1995年,3页
  • 3Cheung D,Vincent T.Efficient mining of association rules in distributed databases[J].IEEE Transactions on Knowledge and Data Engineering,1996,8(6):911-922.
  • 4Agrawal 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.
  • 5Li Shen,Hong Shen,Ling Cheng.New algorithms for efficient mining of association rules[J].Information Sciences,1999,118(4):251-268.
  • 6Bing 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.
  • 7Chen 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.
  • 8王晓峰,尹丹娜,郑诗诠.相关集合论(英文)[J].沈阳化工学院学报,1999,13(1):67-76. 被引量:4
  • 9陆丽娜,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
  • 10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

共引文献126

同被引文献32

  • 1吉根林,杨明,宋余庆,孙志挥.最大频繁项目集的快速更新[J].计算机学报,2005,28(1):128-135. 被引量:47
  • 2钟晓鸣.运用商业智能,提高零售企业竞争力[J].商场现代化,2005(10X):37-38. 被引量:4
  • 3R Agrawal, et al. Mining Association Rules Between Sets of Items in Large Databases[ C ]. Washington: Proceedings of the ACM SIGMOD International Conference Management of Data, 1993. 207-216.
  • 4Han J, Kamber M. Data Mining: Concepts and Technique [ M ]. Beijing: High Education Press,2001. 149-184.
  • 5J Han, J Pei B. Mortazavi-Asl : Frequent Pattern-projected Sequential pattern Mining [ C ]. Proc 2000 Int Conf knowledge Discovery. and Data Mining( KDD' 00 ), Boston, MA ,2000.
  • 6Han J,Jian P,Yiwen Y. Mining Frequent Patterns Without Candidate Generation[ C]. Proceedings of the 2000 ACM SIGMOD International Conference Management of Data. Dallas,2000.1-12.
  • 7Agrawal R, Srikant R. Fast Algorithm for Mining Association Rules[ C]. Proceedings of the 20th lnternation Conference on VLDB, Santiago, 1994. 487 - 499.
  • 8R Agrawal,et al.Mining Association Rules Between Sets of Items in Large Databases[C].Washington:Proceedings of the ACM SIGMOD International Conference Management of Data,1993.207-216.
  • 9Han J,Kamber M.Data Mining:Concepts and Technique[M].Beijing:High Education Press,2001.149-184.
  • 10J Han,J Pei B.Mortazavi-Asl:Frequent Pattern-projected Sequential pattern Mining[C].Proc.2000 Int.Conf.knowledge Discovery and Data Mining(KDD'00),Boston,MA,2000.

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