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

一种基于聚合链的改进FP-Growth算法 被引量：4

An Improved FP-Growth Algorithm Based on Aggregative Chains
在线阅读 下载PDF
导出
摘要 提出了一种基于聚合链挖掘频繁模式的改进FP-growth算法.该算法引入聚合链的单链表结构,改进了FP树结构.改进后的FP树是单向的,每个结点只保留指向父结点的指针,节省了树空间;相同项的不同节点的路径信息压缩进聚合链中,避免了生成节点链和条件模式库.用Agrawa方法生成实验数据进行分析,实验结果验证了该算法在时间上的优势. An improved FP-growth algorithm based on aggregative chains is proposed. A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers to point its children at each node are kept to save the space of tree in comperison with the former one. Route information of different nodes in the same term are compressed into aggregative chains so that the frequent patterns will be produced in aggregative chains without generating node links and conditional pattern bases. Agrawa tests data verified the advantage of time occupancy of the algorithm proposed.
作者 焦明海 姜慧研 唐加福
机构地区 东北大学计算中心 东北大学信息科学与工程学院
出处 《东北大学学报（自然科学版）》 EI CAS CSCD 北大核心 2006年第2期153-156,共4页 Journal of Northeastern University(Natural Science)
基金 辽宁省自然科学基金资助项目(20042020)
关键词 数据挖掘 频繁模式 FP树 聚合链 FP-GROWTH算法 data mining frequent pattem FP tree aggregative chains FP-gmwth algorithm
分类号 TP311.131 [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

  • 1Han J,Kamber M.Data mining:concepts and techniques[M].San Francisco:Academic Press,2001.4-22.
  • 2Agrawa L R,Imielinski T,Swami A.Mining association rules between sets of items in large databases[A].Proceedings of the ACM SIGMOD Conference on Management of Data[C].New York:ACM Press,1993.207-216.
  • 3Aly H H,Taha Y,Amr A A.Fast mining of association rules in large-scale problems[A].Proceedings of the 6th IEEE Symp on Computers and Communications (ISCC 2001)[C].New York:IEEE Computer Society Press,2001.107-113.
  • 4Tsai C F,Lin Y C,Chen C P.A new fast algorithms for mining association rules in large databases[A].Proceedings of the 2002 IEEE International Conference on Systems,Man and Cybernetics(SMC 2002)[C].New York:IEEE Computer Society Press,2002.251-256.
  • 5Han J,Pei J,Yin Y.Mining frequent patterns without candidate generation[A].Proceedings of the ACM SIGMOD Conference[C].Dallas:ACM Press,2000.1-12.
  • 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
  • 7Mohammed J.Mining non-redundant association rules[J].Data Mining and Knowledge Discovery,2004,9(11):223-248.
  • 8张勇,杨玲.一个不需要产生候选集频繁集挖掘算法的研究[J].吉林农业大学学报,2003,25(3):346-349. 被引量：3
  • 9Kantardzic M.Data mining concepts,models,methods,and algorithms[M].Beijing:Tsinghua University Press,2003.151-152.
  • 10杨明,孙志挥.一种基于前缀广义表的关联规则增量式更新算法[J].计算机学报,2003,26(10):1318-1325. 被引量：23

二级参考文献12

  • 1Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases [A]. In : Bunemuu P, Jajodia S// Proceedings of the 1993 SIGMOD Conference on Management of Data[C]. New York : NY ACM Press, 1993 : 207-216.
  • 2Han Jia-wei, Pei Jian,Yin Yi-wen. Mining frequent patterns without candidate generation [A]. In:Dayal U, Gray P M D, Nishio S// Proceedings of the International Conference on Very Large Database[ C ]. San Francisco, CA : Morgan Kanfmann Press, 1999 : 420-431.
  • 3R 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.
  • 4J 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.
  • 5S 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.
  • 6R Agrawal, R Srikant. Mining sequential patterns. In: ICDE'95. Taipei, Taiwan: IEEE Computer Society Press, 1995. 3--14.
  • 7G 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.
  • 8J 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.
  • 9Artur 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.
  • 10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76

同被引文献41

引证文献4

二级引证文献14

东北大学学报（自然科学版）
2006年 第2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