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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概要
- 論文の詳細を見る
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.
- 社団法人電子情報通信学会の論文
- 2004-11-28
著者
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Nguyen Phu
Institute Of Scientific And Industrial Research Osaka University
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WASHIO TAKASHI
Institute of Scientific and Industrial Research, Osaka University
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MOTODA HIROSHI
Institute of Scientific and Industrial Research, Osaka University
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Motoda Hiroshi
Institute Of Scientific And Industrial Research Osaka University
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Motoda Hiroshi
I.s.i.r. Osaka University
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Motoda Hiroshi
Energy Research Laboratory Hitachi Ltd.
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Washio Takashi
Institute Of Scientific And Industrial Research Osaka University
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OHARA KOUZOU
Institute of Scientific and Industrial Research, Osaka University
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Nguyen P
大阪大 産科研
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