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

一种不确定数据集上频繁模式挖掘的近似算法 被引量：8

Approximation algorithm for frequent itemsets mining on uncertain dataset
在线阅读 下载PDF
导出
摘要 为提高不确定数据集上频繁模式挖掘的效率,针对已有算法在判断是否需要为头表中的某项创建子头表时的计算量比较大的问题,给出一个近似挖掘策略AAT-Mine,以损失小部分频繁项集为代价,提高整个算法的挖掘效率。采用三个不同的典型数据集进行了算法的测试,分别与目前最好的算法和典型算法进行性能对比。实验结果验证了近似算法AAT-Mine的时空效率都得到了提高。 To improve the efficiency of frequent itemset mining upon uncertain dataset, addressing the issue of heavy computa- tion cost of existing algorithms on judging whether to build sub header table for a certain item in the header table, this paper proposed an approximation algorithm called AAT-Mine, at the cost of losing a small portion of frequent itemsets, improved the overall mining performance. It evaluated the AAT-Mine algorithm using three datasets against classical and state of art algo- rithms. Experimental results show that AAT-Mine not only outperforms AT-Mine, MBP, IMBP, UF-Growth and CUFP-Mine in terms of running time, but also remains efficient memory usage.
作者 王水 祝孔涛 王乐
机构地区 宁波大红鹰学院信息工程学院 南阳理工学院软件学院 大连理工大学电子信息与电气工程学部计算机科学与技术学院
出处 《计算机应用研究》 CSCD 北大核心 2014年第3期725-728,共4页 Application Research of Computers
基金 国家自然科学基金资助项目(61173163) 宁波市自然科学基金资助项目(2013A610115) 宁波大红鹰学院大宗商品专项项目
关键词 数据挖掘 频繁模式 频繁项集 不确定数据集 近似算法 vdata mining frequent itemsets frequent pattern uncertain dataset approximation algorithm
分类号 TP301.6 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献24

  • 1PRASAD S A, WOLFSON O, CHAMBERLAIN S, et al. Querying the uncertain position of moving objects [ M ]//Temporal Databases : Research and Practice. [ S: 1. ~ :Spring-Verlag,1998:310-337.
  • 2KHOUSSAINOVA N, BALAZINSKA M, SUCIU D. Towards correc- ting input data errors probabilistically using integrity constraints [ C ]//Proc of the 5th ACM International Workshop on Data Enginee- ring for Wireless and Mobile Access. 2006:43-50.
  • 3L~[N Chun-wei, HONG T P. A new mining approach for uncertain da- tabases using CUFP trees [ J]. Expert Systems with Applications, 2012,39(4) :4084-4093.
  • 4AGGARWAL C C, YU P S. A survey of uncertain data algorithms and applications[ J]. IEEE Trans on Knowledge and Data Engi- neering ,2009,21 ( 5 ) :609-623.
  • 5LEUNG C K, MATEO M A F, BRAJCZUK D A. A tree-based ap- proach for frequent pattern mining from uncertain data [ C ]//Proc of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining. [ S. 1. ] : Springer-Verlag,2008:653-661.
  • 6SUN Xiao-ying, LIM Li-ming, WANG Shui. An approximation algo- rithm of mining frequent itemsets from uncertain dataset [ J 1. Interna- tional Journal of Advancements in Computing Technology, 2012,4(3 ) :42-49. i.
  • 7CALDERS T, GARBON1 C, .GOETHALS B. Approximation of fre-quentness probability of itemsets in uncertain data[ C ]//Proc of IEEE International Conference on Data Mining. Washington DC : IEEE Com- puter Society,2010:749-754.
  • 8WANG Liang, CHEUNG D W L, CHENG R, et al. Efficient mining of frequent itemsets on large uncertain databases [ .1 ~. IEEE Trans on Knowledge and Data Engineering,2011,24 ( 12 ) :2170-2183.
  • 9LEUNG C K S, CARMICHAEL C L, HAO Bo-yu. Efficient mining of frequent patterns from uncertain data [ C ]//Proc of IEEE Interna- tional Conference on Data Mining Workshops. Washington DC:IEEE Computer Society,2007:489- 494.
  • 10ZHANG Qin, LI Fei-fei, YI Ke. Finding frequent items in probabilis- tic data [ C ]//Proe of ACM SIGMOD International Conferenee on Management of Data. New York : ACM Press,2008 : 819- 832.

二级参考文献24

  • 1李建中 于戈 周傲英.不确定性数据管理的要求与挑战[J].中国计算机学会通讯,2009,5(4):6-14.
  • 2Aggarwal C C, Yu P S. A framework for clustering uncertain data streams [C] //Proc of the 24th Int Conf on Data Engineering. Los Alamitos, CA: IEEE Computer Society, 2008, 150-159.
  • 3Aggarwal C C. On high dimension projected clustering of uncertain data streams [C] //Proc of the 25th Int Conf on Data Engineering. Los Alamitos, CA: IEEE Computer Society, 2009:1152-1154.
  • 4Zhang Chen, Gao Ming, Zhou Aoying. Tracking high quality clusters over uncertain data streams [C] //Proc of the 1st Workshop on Management and Mining of Uncertain Data (MOUND 2009) Joint with ICDE 2009. Los Alamitos, CA: IEEE Computer Society, 2009 1641-1648.
  • 5Chui C K, Kao B, Hung E. Mining frequent itemsets from uncertain data[G] // LNAI 4426. Berlin: Springer, 2007: 47-58.
  • 6Chui C K, Kao B. A deeremental approach for mining frequent itemsets from uncertain data [G] // LNCS 5012. Berlin: Springer, 2008:64-75.
  • 7Leung C K S, Mateo M A F, Brajczuk D A. A tree-based approach for frequent pattern mining from uncertain data [G] // LNCS 5012. Berlin.. Springer, 2008.. 653-661.
  • 8Leung C K S, Carmichael C L, Hao B. Efficient mining of frequent patterns from uncertain data [G] // LNAI 4426. Berlin: Springer, 2007:489-494.
  • 9Leung C K S, Brajezuk D A. Efficient algorithms for mining constrained frequent patterns from uncertain data [C ]/Proc of SIGKDD Workshop on Knowledge Discovery from Uncertain Data. New York: ACM, 2009:9-18.
  • 10Han Jiawei, Pei Jian, Yin Yiwen. Mining requent patterns without candidate generation [C] //Proc of ACM Int Conf of Management of Data. New York: ACM, 2000 1-12.

共引文献21

同被引文献48

  • 1刘殷雷,刘玉葆,陈程.不确定性数据流上频繁项集挖掘的有效算法[J].计算机研究与发展,2011,48(S3):1-7. 被引量：14
  • 2CHUI C-K, KAO B, HUNG E. Mining frequent itemsets from uncertain data [C] // PAKDD 2007: Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, LNCS 4426. Berlin: Springer, 2007: 47-58.
  • 3WANG L, CHEUNG D W, CHENG R, et al. Efficient mining of frequent itemsets on large uncertain databases [J]. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(12): 2170-2183.
  • 4SUN X, LIM L, WANG S. An approximation algorithm of mining frequent itemsets from uncertain dataset [J]. International Journal of Advancements in Computing Technology, 2012, 4(3): 42-49.
  • 5LEUNG C K, CARMICHAEL C L, HAO B. Efficient mining of frequent patterns from uncertain data [C] // ICDM Workshops 2007: Proceedings of the Seventh IEEE International Conference on Data Mining Workshops. Piscataway: IEEE, 2007: 489-494.
  • 6LEUNG C K, MATEO M A F, BRAJCZUK D A. A tree-based approach for frequent pattern mining from uncertain data [C] // PAKDD 2008: Proceedings of the 12th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, LNCS 5012. Berlin: Springer, 2008: 653-661.
  • 7AGGARWAL C C, LI Y, WANG J, et al. Frequent pattern mining with uncertain data [C] // KDD 2009: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2009: 29-37.
  • 8PEI J, HAN J, LU H, et al. H-mine: Hyper-structure mining of frequent patterns in large databases [C]// ICDM 2001: Proceedings of the 2001 IEEE International Conference on Data Mining. Piscataway: IEEE, 2001: 441-448.
  • 9LIN C W, HONG T P. A new mining approach for uncertain databases using CUFP trees [J]. Expert Systems with Applications, 2012, 39(4): 4084-4093.
  • 10LEUNG C K, TANBEER S K. Fast tree-based mining of frequent itemsets from uncertain data [C]// DASFAA 2012: Proceedings of the 17th International Conference on Database Systems for Advanced Applications, LNCS 7238. Berlin: Springer, 2012: 272-287.

引证文献8

二级引证文献30

计算机应用研究
2014年 第3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