Cl-GBI : A Novel Strategy to Extract Typical Patterns from Graph Data(Graph Data 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.

• 一般社団法人情報処理学会の論文
• 2004-12-04

著者

• Nguyen Phu

  Institute Of Scientific And Industrial Research Osaka University

• WASHIO TAKASHI

  Institute of Scientific and Industrial Research, Osaka University

• MOTODA HIROSHI

  Institute of Scientific and Industrial Research, Osaka University

• Motoda Hiroshi

  Institute Of Scientific And Industrial Research Osaka University

• Motoda Hiroshi

  I.s.i.r. Osaka University

• Motoda Hiroshi

  Energy Research Laboratory Hitachi Ltd.

• Washio Takashi

  Institute Of Scientific And Industrial Research Osaka University

• OHARA KOUZOU

  Institute of Scientific and Industrial Research, Osaka University

• Nguyen P

  大阪大 産科研

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• Extracting Typical Patterns from Graph Data by Chunkingless Graph-Based Induction (特集:「アクティブマイニング」および一般) -- (セッション1 アクティブマイニング:グラフマイニング)
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