Mining of frequent block preserving outerplanar graph structured patterns

Authors:
Yosuke Sasaki;Hitoshi Yamasaki;Takayoshi Shoudai;Tomoyuki Uchida
Affiliations:
Department of Informatics, Kyushu University, Fukuoka, Japan;Department of Informatics, Kyushu University, Fukuoka, Japan;Department of Informatics, Kyushu University, Fukuoka, Japan;Department of Intelligent Systems, Hiroshima City University, Hiroshima, Japan
Venue:
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Year:
2007

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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 semi-structured data like the NCI dataset having about 250,000 chemical compounds can be expressed by outerplanar graphs. In this paper, we consider a data mining problem of extracting structural features from semi-structured data. First of all, we define a block preserving outerplanar graph pattern as an outerplanar graph having structured variables. Then, we present an effective Apriori-like algorithm for enumerating frequent block preserving outerplanar graph patterns from semi-structured data in incremental polynomial time. Lastly, by reporting some preliminary experimental results on a subset of the NCI dataset, we evaluate the performance of our algorithms.