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

挖掘最大频繁项集的遗传蚁群优化算法 被引量：2

Genetic ant colony optimization for mining maximal frequent itemsets
下载PDF
导出
摘要 为了提高挖掘的效率和精度,采用代数定义最大频繁项集并建立其数学模型,通过二进制编码将支持度的计算、蚁群算法和遗传算法求解有机地融合,从而提出一种求解该数学模型的遗传蚁群算法。实验表明,该算法挖掘最大频繁项集是有效的,具有良好的伸缩性。 In order to improve the efficiency and accuracy of mining,adopted the algebraic definition for maximal frequent itemsets and established the mathematical model for it.The computing of support,ant colony algorithm and genetic algorithm were merged organically by the binary code.Thus,this paper proposed a genetic ant colony algorithm to solve this mathematical model.Experimental results show that the proposed algorithm for mining maximal frequent itemsets is effective and scalable.
作者 黄红星 黄习培 王秀丽
机构地区 福建农林大学计算机与信息学院
出处 《计算机应用研究》 CSCD 北大核心 2010年第7期2505-2508,共4页 Application Research of Computers
关键词 关联规则 最大频繁项集 遗传算法 蚁群算法 association rules maximal frequent itemsets genetic algorithm ant colony algorithm
分类号 TP311 [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22

  • 1AGRAWAL R,SRIKANT R.Fast algorithms for mining association rules[C] //Proc of the 20th International Conference on Very Large Database.San Francisco,CA:Morgan Kaufmann,1994:487-499.
  • 2HAN Jia-wei,PEI Jian,YIN Yi-wen.Mining frequent patterns without candidate generation[C] //Proc of 2000 ACM-SIGMOD International Conference on Management of Data.New York:ACM Press,2000:1-12.
  • 3BAYARDO R J.Efficiently mining long patterns from databases[C] //Proc of the ACM SIGMOD International Conference on Management of Data.New York:ACM Press,1998:85-93.
  • 4LIN D I,KEDEM Z M.Pincer-search:a new algorithm for discovering the maximum frequent set[C] //Proc of the 6th European Conference on Extending Database Technology.Heidelberg:Springer-Verlag,1998:105-119.
  • 5GRAHNE G,ZHU J F.High performance mining of maximal frequent itemsets[C] //Proc of the 6th SIAM Int'l Workshop on High Performance Data Mining.2003:135-143.
  • 6GRAHNE G,ZHU J F.Effciently using prefix-trees in mining frequent itemsets[C] //Proc of IEEE ICDM Workshop on Frequent Itemset Mining Implementations.2004.
  • 7HOLLAND J H.Adaptation in natural and artificial system[M].Michigan:University of Michigan Press,1975.
  • 8DORIGO M,MANIEZZO V,COLORNI A.The ant system:optimization by a colony of cooperating agents[J].IEEE Trans on System,Man,and Cybernetics-Part B,1996,26(1):29-41.
  • 9FLOCKHART I W,RADCLIFFE N J.A genetic algorithm-based approach to data mining[C] //Proc of International Conference on KDD.1996:299-302.
  • 10CHOENN R.On the suitability of genetic-based algorithms for data mining[C] //Proc of ER Workshops AIDT.London,UK:Springer-Verlag,1998:55-67.

二级参考文献39

  • 1皮德常,秦小麟,王宁生.基于动态剪枝的关联规则挖掘算法[J].小型微型计算机系统,2004,25(10):1850-1852. 被引量：16
  • 2刘淳安.解非线性规划的多目标遗传算法及其收敛性[J].计算机工程与应用,2006,42(25):27-29. 被引量：2
  • 3葛洪伟,高阳.基于蚁群算法的集合覆盖问题[J].计算机工程与应用,2007,43(4):49-50. 被引量：9
  • 4Agrawal R,Imielinski T,Swami A.Mining association rules between sets of items in large database[C]//Proc of the ACM SIGMOD Intl Conf on Management of Data,Washington D C, 1993 : 207-216.
  • 5Park J S,Chen M S,Yu P S.An effective hash based algorithm for mining association rales[C]//ACM SIGMOD International Conference Management of Data, 1995:175-186.
  • 6Savasere A.Omiecinski E,Navathe S.An efficient algorithm for raining association rules in large database[C]//Proc of 2nd Intl Conf on Very Large Database, Zurich, Swaziland, 1995 : 432-443.
  • 7Pasquier N,Bastide Y,Taouil R,et al.Discovering frequent closed item sets for association rales[C]//ICDT' 99, Israel, 1999 : 398-416.
  • 8Han Jia-wei,Kamber M.Data mining:concepts and techniques[M]. [S.l.] : Morgan Kaufmann Publishers, 2001.
  • 9Pasquoer N,Bastide Y,Taouil R.Efficient mining of association rules using closed itemset lattices[J].Information System,1999,24 ( 1 ) : 25-46.
  • 10Berzal F do,Cubero J C,Marin N.TBAR:an efficient method for association rule mining in relational databases[J].Data & Knowledge Engineering, 2001,37 : 47-64.

共引文献27

同被引文献15

  • 1刘学军,徐宏炳,董逸生,钱江波,王永利.基于滑动窗口的数据流闭合频繁模式的挖掘[J].计算机研究与发展,2006,43(10):1738-1743. 被引量：26
  • 2IBM Research - Almaden E EB/OL]. http..//www almaden, ibm. com.
  • 3Frequent Itemset Mining Dataset Repository[EB/OL] http://fimi, cs. helsinki, fi/data.
  • 4Webb G I. Discovering signi? cant patterns [J] Machine Learning, 2007, 68(1): 1-33.
  • 5Zaki M J, Hsiao C J. CHARM.. An efficient algorithm for closed itemset mining [-C]//Proceedings of the 2nd SIAM International Conference on Data Mining. Arlington, USA: IEEE Computer Society, 2002: 457-473.
  • 6Chi Y, Wang H X, Philip S, et al. Moment.- Maintaining closed frequent itemsets over a data stream sliding window [C]//Proeeedings of the 4th IEEE International Conference on Data Mining. Brighton, UK.. IEEE Press, 2004.. 59-66.
  • 7Leung C K S, Khan Q L DStree: A tree structure for the mining of frequent sets from data streams [C]// Proceedings of the 6th International Conference on Data Mining. Hong Kong, China: IEEE Press, 2006:928-932.
  • 8Jiang N, Gruenwald L. CFFstream: mining closed frequent itemsets in data streams [C]//Proceedings of the 12th ACM SIGKDD International Conference on Knowledge and Data Mining. Philadelphia, USA: ACM Press, 2006.- 592-597.
  • 9Li H F, Ho C C, Lee S Y. Incremental updates of closed frequent itemsets over continuous data streams [J]. Expert Systems with Applications, 2009, a6: 2 451-2 458.
  • 10Jea K F, Li C W. A sliding-window based adaptive approximating method to discover recent frequent itemsets from data streams [C]//Proceedings of the MECS. Hong Kong, China, 2010.

引证文献2

二级引证文献6

计算机应用研究
2010年 第7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