期刊文献+

一种基于频繁模式的关联规则改进算法 被引量:1

An improved algorithm for association rule based on frequent pattern
下载PDF
导出
摘要 分析了基于频繁模式的关联规则算法Fptree,给出了一种基于二进制表示的改进算法,详细介绍了该算法的主要思想,算法实现方案。并通过实例比较了两种算法,证明新算法提高了挖掘规则的效率。 The association algorithm based on frequent pattern was analyzed in this paper, and an improved binary algorithm was put forward. The main idea of algorithm and application were introduced. Comparing two algorithms through some instance certifies that the data mining efficiency was improved by new algorithm.
出处 《河北省科学院学报》 CAS 2006年第3期51-53,共3页 Journal of The Hebei Academy of Sciences
关键词 关联规则 频繁模式 数据挖掘 Association rules Frequent pattern Data mining
  • 相关文献

参考文献8

  • 1Fayyad U M,Piatetsky-Shapiro G,Smyth Petal.Advances in Knowledge Discovery and Data Mining.Menlo Park,CA:AAAI/MIT Press,1996
  • 2Srikant R,Agrawal R.Mining generalized association rules.Dayal U,Gray P M D,Nishio Seds.Proc of the Int'l Conf on Very Large Databases.San Francisco,CA:Morgan Kanfmann Press,1995.406-419
  • 3Agrawal R,Imielinski T,Swami A.Mining association rules between sets of items in large databases.Bunemuu P,Jajodia Seds.Proc of the 1993 ACM SIGMOD Conf on Management of Data.New York,NY:ACM Press,1993.207 -216
  • 4R.Agrawal,etal.Mining association rules between sets of items in large databases.Proc.ACM SIGMOD int 1 conf.management of data,Washington,DC,May 1993,207 -216
  • 5R.Agrawal R.Srikant.Fast algorithms for mining association rules.Proc 20th int 1 conf.very large database,Santiago,Chile,Sept,1994,487-499
  • 6Han J,et al.Mining Frequent Patterns without Candidate Generation,(Slides),In:Proc.2000 ACM-SIGMOD Int.Conf.On Management of Data (SIGMOD 00),Dallas,TX,May2000
  • 7Jiawei Han,Micheline Kambr.DATA MINING Concepts and Techniques.Morgen Kaufmann Publishers
  • 8Sub-Ying Wur,Yungho Leu.An Effective Boolean Algorithm for Mining Association Rules in Large Databases.Department of Information Management National Taiwan University of Science and Technology

同被引文献2

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