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.
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Nagamine, M., Miyahara, T., Kuboyama, T., Ueda, H., Takahashi, K. (2008). Evolution of Multiple Tree Structured Patterns from Tree-Structured Data Using Clustering. In: Wobcke, W., Zhang, M. (eds) AI 2008: Advances in Artificial Intelligence. AI 2008. Lecture Notes in Computer Science(), vol 5360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89378-3_51
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DOI: https://doi.org/10.1007/978-3-540-89378-3_51
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