期刊文献+

一种优化FP-growth的支持度相同项的排序算法 被引量:1

An Optimal FP-growth Algorithm Based on Order of Same Support Item
下载PDF
导出
摘要 Han等人提出了频繁模式增长FP-growth算法,该算法在第一次扫描数据库后,得到频繁项集合和每个频繁项的支持度,并按支持度降序排列,但没有对支持度相同项的排列做进一步说明。本文依据"越是频繁出现,越可能被共享"的建树原则,提出了通过比较该项与前后项项集的支持度,较大者先排列的方法,使后续构建的FP-tree比任意排序构建的FP-tree更优。 Han et al. propose the FP - growth algorithm of Frequent pattern growth, after which scans the database for the first time , obtained the frequent item sets and support of each frequent item, and arranged in descending order ,but don' t illustrate for further for the arrangement of the same support item. This article proposes a method that the first item arranged by comparing the item set support of this item with the before and after, according to the principle of "the more frequent, the more likely to be shared" , so that the latter FP - tree by this method is better than by random arrangement.
作者 武丽芬
出处 《网络新媒体技术》 2012年第4期53-56,共4页 Network New Media Technology
关键词 FP—growth算法 频繁项 项前缀子树 最小支持度 FP - growth algorithm, frequent item, item prefix sub - tree, minimum support
  • 相关文献

参考文献9

  • 1R Agrawal,R Srikant. Fast algorithms for mining association rules[A].Santiago,Chili:VLDB Endowment,1994.487-499.
  • 2J S Park,MS Chen,P S Yu. An effective Hash-based algorithm for mining association rules[A].San Jose:CA:ACM Press,1995.175-186.
  • 3S Brin,R Motwani,C Silverstein. Beyond market basket:Generalizing association rules to correlations[A].Tucson.AZ:ACM Press,1997.265-276.
  • 4R Agrawal,R Srikant. Mining sequential patterns[A].Taipei,Taiwan:IEEE Computer Society Press,1995.3-14.
  • 5G Dong,J Li. Efficient Mining of emerging patterns:Discovering trends and differences[A].San Diego,CA:ACM Press,1999.43-52.
  • 6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
  • 7J Han,J Pei,Y Yin. Mining frequent patterns without candidate generation[A].Dallas:TX:ACM Press,2000.1-12.
  • 8陈志泊.数据仓库与数据挖掘[M]北京:清华大学出版社,2009.
  • 9Agrawal L R. Fast algorithms for mining association rules[A].Santiago:Morgan Kaufmann,1994.487-499.

二级参考文献8

  • 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.

共引文献55

同被引文献4

引证文献1

二级引证文献1

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