期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

一种新的最大频繁项目集挖掘算法 被引量：6

A new algorithm for mining maximal frequent itemsets
下载PDF
导出
摘要 最大频繁项目集挖掘是数据挖掘领域最重要的基本问题之一,在分析已有算法的基础上,提出了一种新的挖掘最大频繁项目集的算法,实验表明该算法在性能上优于已有的同类算法。 Maximal frequent itemsets mining was one of the most important basic problems for itemset mining. Based on the analysis of the existing algorithms, a new algorithm for maximal frequent itemsets mining was presented. Comparative experiments show that the new algorithm outperforms the current algorithms such as MAximal Frequent Itemset Algorithm for transactional database （MAFIA）.
作者 马丽生 邓辉文 齐逸
机构地区 西南大学计算机与信息科学学院
出处 《计算机应用》 CSCD 北大核心 2006年第11期2670-2673,共4页 journal of Computer Applications
关键词 数据挖掘 最大频繁项目集 关联规则 data mining maximal frequent itemsets association roles
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论]
  • 相关文献

参考文献11

  • 1AGRAWAL R , IMIEIJNSKI T , SWAMI A . Mining association rules between sets of items in large database[ A]. Proceedings of 1993 ACM-SIGMOD Conf on Management of (SIGMOD'93) [ C].New York: ACM Press, 1993. 207-216.
  • 2AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules[ A]. Proceedings of 1994 International Conference on Very Large DataBases (VLDB'94) [ C]. San Francisco: Morgan Kaufman,1994. 478-499.
  • 3HAN J, PEI J, YIN Y. Mining frequent patterns without candidate generation[ A]. The 2000 ACM-SIGMOD[ C]. Dallas, TX, 2000.
  • 4LIN D-I., KEDEM ZM. Pincer-search: A new algorithm for discovering the maximun frequent set[ A]. Proceedings of the 6th International Conference on Extending Database Technology[ C]. Valencia,Spain, 1998, 105 - 119.
  • 5BAYARDO RJ. Efficiently mining long patterns from database[ A].Proc of the 1998 ACM SIGMOD International Conference on Management of Data[C]. Seattle, Washington, USA, 1998, 85-93.
  • 6AGGARWAL C, AGRAWAL R, PRASAD VVV. Depth first generation of long patterns[ A]. Proceedings of the 6th ACM SIGKDD International Conference on knowledge Discovery & Data Mining[ C].Boston, MA, USA, 2000,108-118.
  • 7BURDICK D, CALIMLIM M, GEHRKE J. MAFIA: A maximal fiequent itemset algorithm for transactional databases[ A]. Proceedings of the 17th International Conference on Data Engineering[ C]. Heidelberg, Germany, 2001. 443 - 452.
  • 8GRAHNE G, ZHU JF. High performance mining of maximal frequent itemsets [ A]. Proceedings of the 6th SIAM International Workshop on High Performance Data Mining ( HPDM 2003) [ C].2003. 135 - 143.
  • 9刘君强,孙晓莹,王勋,潘云鹤.挖掘最大频繁模式的新方法[J].计算机学报,2004,27(10):1328-1334. 被引量：15
  • 10颜跃进,李舟军,陈火旺.基于FP-Tree有效挖掘最大频繁项集[J].软件学报,2005,16(2):215-222. 被引量：68

二级参考文献21

  • 1[1]Agrawal R., Imielinski T., Swami A.. Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington, D. C. , USA, 1993, 207～216
  • 2[2]Lin D-I. , Kedem Z. M.. Pincer-search: A new algorithm for discovering the maximum frequent set. In: Proceedings of the 6th International Conference on Extending Database Technology, Valencia, Spain, 1998, 105～119
  • 3[3]Bayardo R. J.. Efficiently mining long patterns from databases.In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Seattle, Washington, USA,1998, 85～93
  • 4[4]Aggarwal C. , Agarwal R. , Prasad V. V. V.. Depth first generation of long patterns. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery ＆ Data Mining, Boston, MA, USA, 2000, 108～118
  • 5[5]Burdick D. , Calimlim M. , Gehrke J.. MAFIA: A maximal frequent itemset algorithm for transactional databases. In: Proceedings of the 17th International Conference on Data Engineering, Heidelberg, Germany, 2001, 443～452
  • 6[6]Agrawal R. , Srikant R.. Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, 1994, 487～499
  • 7[7]Liu Jun-Qiang, Pan Yun-He, Wang Ke, Han Jia-Wei. Mining frequent item sets by opportunistic projection. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Alberta, Canada, 2002, 229～238
  • 8[8]Wang Ke, Liu Tang, Han Jia-Wei, Liu Jun-Qiang. Top down FP-growth for association rule mining. In: Proceedings of the 6th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Taipei, Taiwan, China, 2002, 334～340
  • 9宋余庆 朱玉全 孙志辉 陈耿.基于FP—Tree的最大频繁项集挖掘及其更新算法.软件学报,2003,14(9):1586—1592[J].http://wwwjos.org.cn/1000-9825/14/1586.htm,:.
  • 10Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Proc. of the 20th Int'l Conf. on VLDB. 1994. 487-499.http://www.almaden.ibm.conVcs/people/srikant/papers/vldb94.pdf.

共引文献122

同被引文献47

引证文献6

二级引证文献13

计算机应用
2006年 第11期

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