Presentation 2007-05-31
New Structure Similarity and Graph Clustering in Matching Any Graph Characterized by Subgraph Distributions
Takahisa WADA, Hiroyuki OONO, Hiroshige INAZUMI,
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Abstract(in English) As a diversification and increases of a complex structure data, the development of a useful DB system and a new data mining technique is important for effective use of those data. In this paper, we propose new structure similarity in accordance with the feature of target graph sets, and discusses a graph clustering method based on its criterion. Each graph is expressed by the matrix that shows the relation between the node and the subgraph. The similarity is defined by the match of the matrix. In the extraction of a feature structure from the graph sets, Cl-GBI(Chunkingless Graph Based Induction) is used. Because Cl-GBI can control the search space by adjusting to each parameter, structural information can be efficiently used. The examination and the application of the characteristic of the above-mentioned technique are considered.
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Keyword(in English) structure similarity / Cl-GBI(Chunkingless Graph Based Induction) / graph mining / clustering
Paper # AI2007-10
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Committee AI
Conference Date 2007/5/24(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) New Structure Similarity and Graph Clustering in Matching Any Graph Characterized by Subgraph Distributions
Sub Title (in English)
Keyword(1) structure similarity
Keyword(2) Cl-GBI(Chunkingless Graph Based Induction)
Keyword(3) graph mining
Keyword(4) clustering
1st Author's Name Takahisa WADA
1st Author's Affiliation Graduate school of Science and Engineering, Aoyama Gakuin University()
2nd Author's Name Hiroyuki OONO
2nd Author's Affiliation College of Science and Engineering, Aoyama Gakuin University
3rd Author's Name Hiroshige INAZUMI
3rd Author's Affiliation College of Science and Engineering, Aoyama Gakuin University
Date 2007-05-31
Paper # AI2007-10
Volume (vol) vol.107
Number (no) 78
Page pp.pp.-
#Pages 6
Date of Issue