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Evolution of Multiple Tree Structured Patterns from Tree-Structured Data Using Clustering

  • Masatoshi Nagamine
  • Tetsuhiro Miyahara
  • Tetsuji Kuboyama
  • Hiroaki Ueda
  • Kenichi Takahashi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5360)

Abstract

We propose a new genetic programming approach to extraction of multiple tree structured patterns from tree-structured data using clustering. As a combined pattern we use a set of tree structured patterns, called tag tree patterns. A structured variable in a tag tree pattern can be substituted by an arbitrary tree. A set of tag tree patterns matches a tree, if at least one of the set of patterns matches the tree. By clustering positive data and running GP subprocesses on each cluster with negative data, we make a combined pattern which consists of best individuals in GP subprocesses. The experiments on some glycan data show that our proposed method has a higher support of about 0.8 while the previous method for evolving single patterns has a lower support of about 0.5.

Keywords

Positive Data Edge Label Negative Data Single Pattern Tree Structure Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Masatoshi Nagamine
    • 1
  • Tetsuhiro Miyahara
    • 1
  • Tetsuji Kuboyama
    • 2
  • Hiroaki Ueda
    • 1
  • Kenichi Takahashi
    • 1
  1. 1.Graduate School of Information SciencesHiroshima City UniversityHiroshimaJapan
  2. 2.Computer CenterGakushuin UniversityTokyoJapan

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