期刊文献+

频繁集挖掘算法研究 被引量:2

Research on Frequent Itemsets Mining Algorithm
下载PDF
导出
摘要 归纳分析了关联规则的典型挖掘算法及其思想,并通过实例比较各算法之间的差别,同时讨论了各种算法的优化技术,分析了他们的适应性及优缺点。 This dissertation summarizes typical mining algorithms and their basic ideas. The differences among these algorithms are compared and the consequences are illustrated through examples. Some techniques to promote algorithm's efficiency are also discussed with their advantages and disadvantages.
作者 谢廷婷
出处 《计算机与现代化》 2007年第3期60-63,共4页 Computer and Modernization
基金 福建工程学院科研发展基金资助项目(GY-Z0563)
关键词 关联规则 APFIORI算法 散列 采样 事务压缩 FP-树 association rule Apfiori hash sample transaction reduction FP-tree
  • 相关文献

参考文献3

二级参考文献8

  • 1[1]Agrawal R, Imielinski T, Swami A. Mining Association Rules Between Sets of Items in Large Database. Washington, DC: In Proc. 1993 ACMSIGMOD Int. Conf. Management of Data, 1993-05:207-216
  • 2[2]Park J S, Chen M S, Yu P S. An Effective Hash Based Algorithm for Mining Association Rules. San Jose, CA:In Proc. 1995 ACM-SIGMOD Int. Conf. Management of Data, 1995-05:175-186
  • 3[3]Han J, Pei J, Yin Y. Mining Frequent Patterns Without Candidate Generation. Dallas,TX:In Proc.2000 ACM-SIGMOD Iht. Conf.Management of Data, 2000-05:1-12
  • 4[4]Han J, Kambe M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., 2001
  • 5R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. Of the 20th Int'l Conference on Very Large Databases (VLDB'94) ,pages 487-499,Santiago, Chile, September 1994.
  • 6Hannu Toivonen. Sampling large databases for association rules. In teh VLDB Journal, pages 134- 145,1996.
  • 7S. Brin, R. Motwani, J. D. Ullman, and S. Tsur. Dynamic itemset counting and impliction rules for market basket data. SIGMOD Record, 6(2) :255-264,June 1997.
  • 8Noga Alon and Joel H. Spencer. The Probabilistic Method. John Wiley Inc. , New York, NY, 1992.

共引文献13

同被引文献17

引证文献2

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