A Polynomial Time Algorithm for Finding Linear Interval Graph Patterns

Yamasaki, Hideo; Shoudai, Takayoshi

doi:10.1007/978-3-540-72504-6_6

Cited by 5 publications

(1 citation statement)

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“…Then by applying their algorithm to the NCI dataset [4], they found typical subgraph structures of chemical compounds. Yamasaki and Shoudai [9] proposed an interval graph pattern and presented a polynomial time algorithm for finding a minimally generalized interval graph pattern explaining a given finite set of interval graphs. As other related works, in the framework of inductive inference, by Suzuki et al [6] and Takami et al [7] gave polynomial time learning algorithms for tree patterns with internal structured variables and two-terminal series parallel graph patterns, respectively.…”

Section: Introductionmentioning

confidence: 99%

Mining of Frequent Block Preserving Outerplanar Graph Structured Patterns

Sasaki

Yamasaki

Shoudai

et al.

Inductive Logic Programming

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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 like the NCI dataset. For this data mining problem, first of all, we define a new graph pattern, called 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.

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Section: Introductionmentioning

confidence: 99%