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Eclat与Eclat+算法的比较分析 被引量：1

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摘要 深入分析了频繁模式挖掘算法Eclat和Eclat+,在大数据集上挖掘长模式时,Eclat+的性能不及Eclat。
作者 刘井莲
机构地区 绥化学院计算机系
出处 《绥化学院学报》 2010年第2期189-190,共2页 Journal of Suihua University
基金 绥化学院杰出青年基金
关键词 长模式 广度优先搜索 垂直数据表示
分类号 TP312 [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4

  • 1R. Agrawal, T. Imielinski, A. Swami. Mining association rules between sets of items in large databases, Proc. of the ACMSIGMOD 1993 Int'l Conference on Management of Data, Washington D.C., May 1993.
  • 2李敏,李春平.频繁模式挖掘算法分析和比较[J].计算机应用,2005,25(B12):166-171. 被引量：11
  • 3MJZaki, SParthasarathy, M.Ogihara, and W.Li. New algorithms for fast discovery of association rules, In Proc.of the 3rd Int'l Conf.on KDD and Data Mining (KDD'97), Newport Beach, California, August 1997.
  • 4宋长新,马克.改进的Eclat数据挖掘算法的研究[J].微计算机信息,2008,24(24):92-94. 被引量：17

二级参考文献18

  • 1龙银香.移动计算环境下的数据挖掘研究[J].微计算机信息,2005,21(07X):35-38. 被引量：17
  • 2HAND D, MANNILA H, SMYTH P. Principles of Data Mining[ M]. Massachusetts Institute of Technology, 2001.
  • 3MANNIEA H. Methods and problems in data mining[ A]. Proceedings of the 6th International Conference on Database Theory[ C],1997.41 -55.
  • 4KRISHNAMURTHY R, IMIELINSKI T. Practitioner Problems in Need of Database Research: Research Directions in Knowledge Discovery[A]. Vol. 20, No. 3 of SIGMOD Record[ C], Sept. 1991.76-78.
  • 5HAN J, KAMBER M. Data Mining: Concepts and Techniuqes[ M].Morgan Kaufmann Publishers, San Francisco, CA, 2001.
  • 6AGRAWAL R, IMIELINSKI T, A. Swami. Mining association rules between sets of items in large databases[ A]. Proceedings of ACM SIGMOD International Conference on Management of data [ C],1993. 207-216.
  • 7AGRAWAL R, SRIKANT R. Fast algorithms for mining association rules[ A]. In J. B. Bocca, M. Jarke, and C. Zaniolo, editors, Proceedings of the 20th International Confererice on Very Large Database[C], MorganKaufmann, 1994. 487 -499.
  • 8PARK JS, CHEN MS, YU PS. An effective hash - based algorithm for mining association rules[ A]. Proceedings of ACM SIGMOD International Conference on Management of Data[ C], San Jose, CA,May 1995. 175 - 186.
  • 9TOIVONEN H. Sampling large databases for association rules[ A].Proceedings of the 22nd International Conference on Very Large Database[C], Bombay, India, September 1996. 134-145.
  • 10BRIN S, MOTWANI R, ULLMAN JD, TSUR S. Dynamic itemset counting and implication rules for market basket data[ A]. In Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data[ C], 1997. 255 -264.

共引文献22

同被引文献10

  • 1李敏,李春平.频繁模式挖掘算法分析和比较[J].计算机应用,2005,25(B12):166-171. 被引量：11
  • 2AGRAWAL R, SRIKANT R. Fast Algorithms for min- ing association rules [C]// Proceedings of 20th Interna- tional Conference on Very Large Data Bases. Santiago, Chile: Morgankaufman, 1994:487 - 499.
  • 3HAN J, PEI J, YIN Y. Mining frequent patterns with- out candidate generation [C]/// Proeeedlngs of the 2000 ACM Data. Dallas, United States: ACM, 2000:1-12.
  • 4FENG Pei-en, ZHANG Hui, QIU Qing-ying, et al. PCAR: an efficient approach for mining association rules [C]/// Proceedings of the ICNC-FSKD 2008 Inter- national Conference on Fussy Systems and Knowledge Dis- covery. Jinan: IEEE, 2008:605-609.
  • 5ZAKI M J. Scalable algorithms for association mining[J]. IEEE Transactions on Knowledge and Data Engi- neering, 2000,12(3) : 372- 390.
  • 6ZAKI M J. Fast vertical mining using diffsets [R]. Technical Report 01-1, Troy, New York: Rensselaer Polytechnic Institute. 2001.
  • 7HAN J, KAMBE M. Data mining: concepts and Tech- niques [M]. San Francisco, United States: Morgan Kaufmann Publishers Inc, 2001 : 231.
  • 8GOETHALS B. Frequent itemset mining dataset re- pository [EB/OL]. [2004-12-2]. http://fimi, ua. ac.be/data/.
  • 9宋长新,马克.改进的Eclat数据挖掘算法的研究[J].微计算机信息,2008,24(24):92-94. 被引量：17
  • 10熊忠阳,陈培恩,张玉芳.基于散列布尔矩阵的关联规则Eclat改进算法[J].计算机应用研究,2010,27(4):1323-1325. 被引量：18

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二级引证文献13

绥化学院学报
2010年 第2期

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