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极大频繁子树挖掘及其应用 被引量：4

Maximum Frequent Tree Mining and its Applications
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摘要 极大频繁子树挖掘在Web挖掘、HTML/XML文档分析、生物医学信息处理等领域有着重要的应用,可用于解决这些领域的自同构问题。本文提出了一种极大频繁子树挖掘算法(MFTM)。MFTM基于最右路径扩展技术,在搜索过程中,采用覆盖定理进行裁剪,压缩搜索空间,从而极大地加快了算法的收敛速度。性能实验表明,极大频繁挖掘等算法是有效和可伸缩的。 A novel algorithm called Maximum Frequent Tree Mining （MFTM） is presented to discover maximum frequent sub-trees from forest. MFTM uses the right-most path expansion technique. The Overlay Theorem is proposed to reduce the search space and accelerate the convergence speed. We conduct detailed experiments to test the perform- ance and scalability of the methods. The experiments demonstrate that MFTM is effective and scalable. MFTM can be applied to solve the isomorphic problems in the domains such as Web mining, HTML/XML data analysis, bioinformatics, and so on.
作者 杨沛 谭琦
机构地区 华南理工大学计算机应用研究所 华南师范大学计算机学院
出处 《计算机科学》 CSCD 北大核心 2008年第2期150-153,共4页 Computer Science
基金 国家自然科学基金(60003019)资助
关键词 频繁子树挖掘 WEB挖掘 信息抽取 Frequent tree mining, Web mining, Data extraction
分类号 TP311.13 [自动化与计算机技术—计算机软件与理论] 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12

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二级参考文献13

  • 1Cook D, Holder L. Substructure discovery using minimal description length and background knowledge. Journal of Arti_cial Intelligence Research, 1994,1: 231～ 255.
  • 2Yoshida K, Motoda H. CLIP: Concept learning from inference patterns. Artificial Intelligence, 1995,75 (1):63～ 92.
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共引文献4

同被引文献21

  • 1朱永泰,王晨,洪铭胜,汪卫,施伯乐.ESPM——频繁子树挖掘算法[J].计算机研究与发展,2004,41(10):1720-1727. 被引量：18
  • 2赵传申,孙志挥,张净.基于投影分支的快速频繁子树挖掘算法[J].计算机研究与发展,2006,43(3):456-462. 被引量：14
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  • 6L Zou,Y Lu,H Zhang,R Hu.Mining Frequent Induced Subtree Patterns with Subtree-Constraint.Proceedings of the 6th IEEE International Conference on Data Mining-Workshops (ICDMW2006),Hongkong,China,December,2006:3-7.
  • 7H Tan.T S Dillon,F Hadzic,E Chang,L Feng.IMB3-Miner Mining Induced/Embedded Subtrees by Constraining the Level of Embedding.Proceedings of the Pacific-Asia Conference on Knowledge.Discovery and Data Mining,Singapore,2006:450-461.
  • 8M Seno,G Karypis.Finding Frequent Patterns Using LengthDecreasing Support Constraints.Data Mining and Knowledge Discovery,2005,10(3):197-228.
  • 9Y.Chi,Y.Yang,Y.Xia,R.R.Muntz,CMTreeMiner,Mining Closed and Maximal Frequent Subtrees from Databases of Labeled Rooted Trees.Lecture Notes in Computer Science,2004,3056:63-73.
  • 10M. J. Zaki. CSLOGS Data. 2003-8-6[2007-11-1] http:// www.cs.rpi.edu/~zaki/software.

引证文献4

二级引证文献3

计算机科学
2008年 第2期

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