Learning Block-Preserving Outerplanar Graph Patterns and Its Application to Data Mining

  • Authors:
  • Hitoshi Yamasaki;Yosuke Sasaki;Takayoshi Shoudai;Tomoyuki Uchida;Yusuke Suzuki

  • Affiliations:
  • Department of Informatics, Kyushu University, Fukuoka, Japan 819-0395;Department of Informatics, Kyushu University, Fukuoka, Japan 819-0395;Department of Informatics, Kyushu University, Fukuoka, Japan 819-0395;Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan 731-3194;Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan 731-3194

  • Venue:
  • ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
  • Year:
  • 2008

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Abstract

An outerplanar graph is a planar graph which can be embedded in the plane in such a way that all of vertices lie on the outer boundary. Many chemical compounds are known to be expressed by outerplanar graphs. We proposed a block preserving outerplanar graph pattern (bpo- graph pattern, for short) as a graph pattern common to a set of outerplanar graphs like a dataset of chemical compounds. In this paper, firstly we give a polynomial time algorithm for finding a minimally generalized bpo- graph pattern explaining a given set of outerplanar graphs. Secondly we give a pattern mining algorithm for enumerating all maximal frequent bpo- graph patterns in a set of outerplanar graphs. Finally, in order to show the performance of the pattern mining algorithm, we report experimental results on chemical datasets.