Presentation 2003/7/10
Generalization for Frequent Subgraph Mining
Akihiro INOKUCHI, Takashi WASHIO, Hiroshi MOTODA,
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Abstract(in English) Data mining to derive frequent subgraphs from a dataset of general graphs has high computational complexity because it includes the explosively combinatorial search for candidate subgraphs and subgraph isomorphism matching. Although some approaches have been proposed to derive characteristic patters from graph structured data, they limit the graphs to be searched within a specific class. In this paper, we propose an approach to conduct a complete search of various classes of frequent subgraphs in a massive dataset of labeled graphs within practical time. The power of our approach comes from the algebraic representation of graphs, its associated operations and well-organized bias constraints to limit the search space efficiently. It performance has been evaluated through real world datasets, and the high scalability of our approach has been confirmed with respect to the amount of data and the computation time.
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Keyword(in English) Graph Mining / AGM Algorithm / Frequent Subgraph / Canonical Form / Adjacency Mtrix / Bias
Paper # DE2003-49
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Committee DE
Conference Date 2003/7/10(1days)
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Registration To Data Engineering (DE)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Generalization for Frequent Subgraph Mining
Sub Title (in English)
Keyword(1) Graph Mining
Keyword(2) AGM Algorithm
Keyword(3) Frequent Subgraph
Keyword(4) Canonical Form
Keyword(5) Adjacency Mtrix
Keyword(6) Bias
1st Author's Name Akihiro INOKUCHI
1st Author's Affiliation Tokyo Research Laboratory, IBM Japan:Institute of Scientific and Industrial Research, Osaka Univ.()
2nd Author's Name Takashi WASHIO
2nd Author's Affiliation Institute of Scientific and Industrial Research, Osaka Univ.
3rd Author's Name Hiroshi MOTODA
3rd Author's Affiliation Institute of Scientific and Industrial Research, Osaka Univ.
Date 2003/7/10
Paper # DE2003-49
Volume (vol) vol.103
Number (no) 191
Page pp.pp.-
#Pages 6
Date of Issue