期刊文献+

一种新的基于投影的频繁模式树构造算法

下载PDF
导出
摘要 本文分析FP-growth算法存在的主要问题,提出了一种新的基于投影的频繁模式树构造算法。该算法充分利用大型数据库的投影运算能力,按层来构造频繁模式树(FP-tree),有效地解决了传统的FP-tree构造中存在的问题。实验结果表明,本文的算法与传统的频繁模式树的构造算法相比,具有比较好的时间和空间的可伸缩性。
出处 《计算机科学》 CSCD 北大核心 2006年第B12期136-138,177,共4页 Computer Science
基金 广西“新世纪十百千人才工程”专项基金项目(桂人字2001213号)和广西自然科学基金项目(桂科自0229008)联合资助.
  • 相关文献

参考文献11

  • 1Han J, Kamber M. Data Mining: Concepts and Techniques.Morgan Kaufman, San Francisco, CA, 2001
  • 2Agrawal R,Imielinski T,Swami A. Mining association rules between sets of items in large databases. In: Proceedings of 1993ACM-SIGMOD International Conference on Management of Data,Washington,D. C. ,May 1993. 207-216
  • 3Agrawal R, Srikant R. Fast algorithms for mining association rules in large databases. In: Bocca JB, Jarke M, Zaniolo C,eds. Proc. of the 20th Int'l Conf. on Very Large Data Bases.Santiago: Morgan Kaufmann, 1994. 478-499
  • 4Han J, Pei J, Yin Y. Mining Frequent Patterns without Candidate Generation. In:Proceedings of the 2000 ACM-SIGMOD International Conference on Management of Data, Dallas, TX:ACM Press,2000. 1-12
  • 5Pei Jian, Han Jiawei, Mao Runying. Closet; An efficient algorithm for mining frequent closed itemsets. In: SIGMOD International Workshop on Data Mining and Knowledge Discovery,May 2000
  • 6Pei Jian, Han Jiawei, Wang Jianyong. Closet+:Searching forthe best strategies for mining frequent closed itemsets. In:SIGKDD'03, August 2003
  • 7Lucchese C, Orlando S, Perego R. Fast and Memory Efficient Mining of Frequent Closed Itemsets: [Technical Report CS-2004-9]. Nov 2004
  • 8杨红菊,梁吉业.一种挖掘频繁项集和频繁闭包项集的算法[J].计算机工程与应用,2004,40(13):176-178. 被引量:5
  • 9Mohammad EI-Hajj, Zaiane O R, COFI-tree Mining:A NewApproach to Pattern Growth with Reduced Candidaec Generation. In..Workshop on Frequent Itemset Mining Implementations(FIMI'03) in conjunction with IEEE-ICDM 2003, Melbourne,Florida, USA,November 2003
  • 10范明,李川.在FP-树中挖掘频繁模式而不生成条件FP-树[J].计算机研究与发展,2003,40(8):1216-1222. 被引量:56

二级参考文献15

  • 1R Agrawal, R Srikant. Fast algorithms for mining association rules. In: Proc of 1994 Int'l Conf on Very Large Data Bases.Santiago, Chili: VLDB Endowment, 1994. 487--499.
  • 2J S Park, M S Chen, P S Yu. An effective Hash-based algorithm for mining association rules. In: Proc of 1995 ACM-SIGMOD Int'l Cord on Management of Data. San Jose, CA: ACM Press,1995. 175--186.
  • 3S Brin, R Motwani, C Silvemtein. Beyond market basket:Generalizing association rules to correlations. In: Proe of 1997 ACM-SIGMOD Int'l Conf on Management of Data. Tucson, AZ:ACM Press, 1997. 265--276.
  • 4R Agrawal, R Srikant. Mining sequential patterns. In: ICDE'95. Taipei, Taiwan: IEEE Computer Society Press, 1995. 3--14.
  • 5G Dong, J Li. Efficient mining of emerging patterns: Discovering trends and differences. In: Proc of the 5th ACM SIGKDD Int'l Conf on Knowledge Discovery and Data Mining. San Diego, CA:ACM Press, 1999. 43~52.
  • 6J Han, J Pei, Y Yin. Mining frequent patterns without candidate generation. In: Proe of 2000 ACM-SIGMOD Int'l Conf on Management of Data. Dallas, TX: ACM Press, 2000. 1--12.
  • 7Artur Bykowski, Christophe Rigotti. A eondemsed representation to find frequent patterns. In: Proe of the 20th ACM SIGACT-SIGMOD-SIGART Symp on Principles of Database Systems(PODS 2001). Santa Barbara, CA: ACM Press, 2001. 267~273.
  • 8范明 等.数据挖掘:概念与技术[M].北京:机械工业出版社,2001.8.
  • 9R Agrawal,T Imielinski,A Swami. Mining association rules between sets of items in large databases[C].In:The ACM SIGMOD Int′l Conf Management of Data, Washington D C, 1993: 207~216
  • 10Rakesh Agrawal,Ramakrishnan Srikant. Fast algorithms for mining association rules[C].In:Proceedings of the 20th International Conference on Very Large Databases,Santigo,Chile, 1994:487~499

共引文献58

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部