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

Authors:
Masatoshi Nagamine;Tetsuhiro Miyahara;Tetsuji Kuboyama;Hiroaki Ueda;Kenichi Takahashi
Affiliations:
Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan 731-3194;Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan 731-3194;Computer Center, Gakushuin University, Tokyo, Japan 171-8588;Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan 731-3194;Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan 731-3194
Venue:
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2008

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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.