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Title:
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LEARNING OF MULTIPLE TREE STRUCTURED PATTERNS USING CLUSTERING AND EVOLUTION |
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Author(s):
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Yoshiaki Otsuka, Tetsuhiro Miyahara, Tetsuji Kuboyama |
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ISBN:
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978-972-8939-47-2 |
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Editors:
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Miguel Baptista Nunes, Pedro Isaías and Philip Powell |
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Year:
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2011 |
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Edition:
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Single |
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Keywords:
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Learning, knowledge discovery, clustering, genetic programming, tree structured data |
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Type:
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Short Paper |
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First Page:
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227 |
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Last Page:
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231 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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We propose a learning method for extracting multiple tree structured patterns from tree structured data by using clustering and evolutionary method (Genetic Programming, GP). We use a set of multiple tree structured patterns, called tag tree patterns, as a combined pattern. A structured variable in a tag tree pattern can be substituted by an arbitrary tree. A set of multiple tag tree patterns matches a tree, if at least one of the set of patterns matches the tree. By clustering of positive data and by running GP subprocesses on each cluster with negative data, we make a combined pattern which consists of best individuals in GP subprocesses. |
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