期刊文献+

挖掘关联规则的并行算法研究 被引量:7

Study on Parallel Algorithms for Association Rule Mining
下载PDF
导出
摘要 对挖掘关联规则的算法进行了简单的回顾 ,分析了已有的挖掘关联规则算法的不足 。 This paper reviews the association rule mining algorithms, and analyzes the shortage about the existed algorithms. Introduced some parallel algorithms for association rule mining.
出处 《计算机应用研究》 CSCD 北大核心 2002年第2期9-11,共3页 Application Research of Computers
关键词 关联规则 并行算法 集群 数据挖掘 数据库 Association Rule Parallel Algorithms Lattice Cluster
  • 相关文献

参考文献1

二级参考文献10

  • 1Lakshmanan L V S,Proc 1999 ACMSIGMOD Conf on Management of Data,1999年
  • 2Ng R,Proc 1999 ACMSIGMOD Conf on Management of Data,1999年
  • 3Ng R,Proc of 1998 ACMSIGMOD Conf on Management of Data,1998年,13页
  • 4Han J,1996 SIGMOD’96 Workshop on Research Issueson Data Miningand Knowledge Discovery(DMKD’96),1996年
  • 5Chen M S,IEEE Trans Knowledge Data Engineering,1996年,8卷,6期,866页
  • 6Shen Weimin,Advances Knowledge Discovery Data Mining,1996年,375页
  • 7Fu Y,Proc 1995 Int’l Workshop on Knowledge Discovery and Deductive and Object Oriented Databases(KDOOD’95),1995年,39页
  • 8Han J,Proc of the 21st international conference on verylarge databases,1995年,420页
  • 9冯玉才,冯剑琳.关联规则的增量式更新算法[J].软件学报,1998,9(4):301-306. 被引量:227
  • 10张朝晖,陆玉昌,张钹.发掘多值属性的关联规则[J].软件学报,1998,9(11):801-805. 被引量:61

共引文献47

同被引文献46

  • 1胡吉明,鲜学丰.挖掘关联规则中Apriori算法的研究与改进[J].计算机技术与发展,2006,16(4):99-101. 被引量:59
  • 2韩家炜(加)等 范明等译.数据挖掘:概念与技术[M].机械工业出版社,2001..
  • 3[1]R Agrawal, T Imielinski, A Swami. Mining Association Rules between Sets of Items in Large Databases [C]. In Proc. of the ACM SIGMOD Int' I Conf. on Management of Data (ACM SIGMOD'93), Washington, USA,1993.207-216.
  • 4[2]R Agrawal et al. Fast Algorithms for Mining Association Rules [C]. In Proc. of the 20th Int'l Conf. on Very Large Databases (VLDB'94), Santiago, Chile, 1994.487-499.
  • 5[3]R Agrawal,R Srikant. Mining Sequential Patterns[C]. In Proc.of the Int'l Conf. on Data Engineering (ICDE), Taipei,Taiwan, 1995.3-14.
  • 6[4]N F Ayan, A U Tansel, et al. Arkun. An Efficient Algorithm to Update Large Itemsets with Early Pruning[C]. In Proc. of the 5th Int'l Conf. on Knowledge Discovery and Data Mining (KDD'99), San Diego, California, USA,1999.439-450.
  • 7[5]R J Bayardo Jr, et al. Constraint-based Rule Mining in Large,Dense Databases[C]. In Proc. of the 15th Int'l Conf. on Data Engineering, Sydney, Australia, 1999.85-93.
  • 8[6]S Brin, R Motwani,C Silverstein. Beyond Market Baskets:Generalizing Association Rules to Correlations [C]. In Proc. of the ACM SIGMOD Iht'1 Conf. on Management of Data (ACM SIGMOD ' 97), 1997.265-276.
  • 9[7]S Brin, et al. Dynamic Itemset Counting and Implication Rules for Market Basket Data[C]. In Proc. of the ACM SIGMOD Int'I Conf. on Management of Data, 1997. 123-140.
  • 10[8]T Fukuda, Y. Morimoto, et al. Mining Optimized Association Rules for Numeric Attributes [C]. In Proc. of the 15th ACM SIGACT-SIGMOD-SIGART Symp. on Principles of Database Systems (PODS'96), Montreal, Canada, 1996.23-32.

引证文献7

二级引证文献40

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