Presentation 2004/11/28
Cl-GBI : A Novel Strategy to Extract Typical Patterns from Graph Data(Graph Data Mining)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
PHU CHIEN NGUYEN, KOUZOU OHARA, HIROSHI MOTODA, TAKASHI WASHIO,
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Abstract(in English) A machine learning technique called Graph-Based Induction (GBI) extracts typical patterns from graph data by stepwise pair expansion (pair-wise chunking). Because of its greedy search strategy, it is very efficient but suffers from incompleteness of search. Also, it cannot give the correct number of occurrences as well as the positions of patterns in each transaction of the graph data. Improvement is made on its search capability by using a new search strategy, where frequent pairs are never chunked but used as pseud-nodes in the subsequent steps, thus allowing extraction of overlapping subgraphs. This new algorithm, called Cl-GBI (Chunkingless Graph-Based Induction), was tested against two datasets, the promoter dataset from UCI repository and the hepatitis dataset provided by Chiba University, and shown successful in extracting more typical substructures.
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Paper # AI2004-37
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Committee AI
Conference Date 2004/11/28(1days)
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Language ENG
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Title (in English) Cl-GBI : A Novel Strategy to Extract Typical Patterns from Graph Data(Graph Data Mining)(Joint Workshop of Vietnamese Society of AI, SIGKBS-JSAI, ICS-IPSJ, and IEICE-SIGAI on Active Mining)
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1st Author's Name PHU CHIEN NGUYEN
1st Author's Affiliation Institute of Scientific and Industrial Research, Osaka University()
2nd Author's Name KOUZOU OHARA
2nd Author's Affiliation Institute of Scientific and Industrial Research, Osaka University
3rd Author's Name HIROSHI MOTODA
3rd Author's Affiliation Institute of Scientific and Industrial Research, Osaka University
4th Author's Name TAKASHI WASHIO
4th Author's Affiliation Institute of Scientific and Industrial Research, Osaka University
Date 2004/11/28
Paper # AI2004-37
Volume (vol) vol.104
Number (no) 486
Page pp.pp.-
#Pages 6
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