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压缩频繁序列模式集

Compressing the Set of Frequent Sequential Patterns
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摘要 压缩频繁序列模式集是针对频繁序列模式的全集太大这个问题的一种解决方法.为了得到高质量的压缩效果,先对频繁序列模式聚簇,再从每个簇中挑选出有代表性的序列模式,使这些有代表性的序列模式的数目尽可能地少.一个贪婪算法和一个基于候选集的快速算法是压缩频繁序列模式集的有效算法.有代表性的序列模式集合是频繁序列模式的一种子集,实验结果表明它能取得很好的压缩效果. Compressing the set of frequent sequential patterns is a method in order to address the problem of explosive number of output sequential patterns. In order to get high-quality compression, it first clusters frequent sequential patterns, and then select and output only a representative sequential pattern for each cluster such that the number of these representative sequential patterns is minimized. A greedy algorithm and an efficient candidate_based algorithm are proposed. The set of representative sequential patterns is a kind of subset of frequent sequential patterns. Experimental results show that it can achieve very good compression effect.
作者 王涛
机构地区 湖北经济学院计算机科学与技术学院
出处 《小型微型计算机系统》 CSCD 北大核心 2008年第3期503-507,共5页 Journal of Chinese Computer Systems
关键词 数据挖掘 序列模式 有代表性的序列模式 data mining sequential pattern representative sequential pattern
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
作者简介 王涛，女，1969年生，博士，研究方向为数据挖掘、特种数据库、软件测试．E—mail：imwt@21cn．com
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参考文献7

  • 1Yan X, Han J, Afshar R. Clospan: mining closed sequential patterns in large databases[A]. In: Chakrabarti ed. Proc, 2003 SIAM Int. Conf. Data Mining (SDM' 2003). San Francisco [C]. 2003. San Fransisco: SIAM Press, 2003,166-177.
  • 2Wang J, Han J. BIDE: efficient mining of frequent closed sequences[C]. In: Proc. 2004 Int. Conf. on Data Engineering (ICDE' 04). Boston. 2004. Los Alamitos: IEEE Computer Society, 2004,79-90.
  • 3王涛,卢炎生.使用序列模式精简基挖掘序列模式[J].小型微型计算机系统,2005,26(10):1810-1815. 被引量：4
  • 4Tzvetkov P, Yan X, Han J. Tsp: mining top-k closed sequential patterns [ A ]. In: Proc. 2003 Int. Conf. Data Mining (ICDM' 03). Melbourne, FL. 2003[C]. Washington D. C. , USA: IEEE Press, 2003, 441-448.
  • 5Kum H C, Pei J, Wang W. ApproxMAP: approximate mining of consensus sequential patterns [A]. In: Proceedings of the 2003 SIAM International Conference on Data Mining (SIAM DM '03). San Francisco, CA. 2003[C]. London: Springer, 2003, 316-320.
  • 6Dong Xin, Han Jia-wei, Yan Xi-feng, et al. Mining compressed frequent-pattern sets [A]. In: Proceedings of the 31st VLDB Conference. Trondheim[C], Norway. 2005. CA, USA: Morgan Kaufmann, 2005, 709-720.
  • 7Pei J, Dong G, Zou W,et al. On computing condensed frequent pattern bases[A]. In: Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM' 2002)[C]. Macbashi, 2002. Washington D. C., USA: IEEE Press, 2002, 301-322.

二级参考文献8

  • 1Agrawal R, Srikant R. Mining sequential patterns [C]. In ICDE'95, 3-14, Taipei, Taiwan, Mar. 1995.
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  • 7Pei J, Dong G, Zou W et al. On computing condensed frequent pattern bases[C]. In: Proc. of Int. Conf. on Data Mining,Maebashi. Japan, December 2002.
  • 8Yan X, Han J, Afshar R. CloSpan: Mining closed sequential patterns in large databases[C]. In: SDM'03, San Francisco,CA, May 2003.

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小型微型计算机系统
2008年 第3期

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