Presentation 2003/9/8
A Graph-Based Approach for Temporal Relationship Mining
Ryutaro ICHISE, Masayuki NUMAO,
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Abstract(in English) In managing medical data, handling time-series data, which contain irregularities, presents the greatest difficulty. In the present paper, we propose a first-order rule discovery method for handling such data. The present method is an attempt to use graph structure to represent time-series data and reduce the graph using specified rules for inducing hypothesis. In order to evaluate the proposed method, we conducted experiments using real-world medical data.
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Keyword(in English) Machine learning / Temporal mining / Active mining / Inductive Logic Programming
Paper # AI2003-52
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Conference Information
Committee AI
Conference Date 2003/9/8(1days)
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Registration To Artificial Intelligence and Knowledge-Based Processing (AI)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Graph-Based Approach for Temporal Relationship Mining
Sub Title (in English)
Keyword(1) Machine learning
Keyword(2) Temporal mining
Keyword(3) Active mining
Keyword(4) Inductive Logic Programming
1st Author's Name Ryutaro ICHISE
1st Author's Affiliation Intelligent Systems Research Division, National Institute of Informatics()
2nd Author's Name Masayuki NUMAO
2nd Author's Affiliation The Institute of Scientific and Industrial Research, Osaka University
Date 2003/9/8
Paper # AI2003-52
Volume (vol) vol.103
Number (no) 305
Page pp.pp.-
#Pages 6
Date of Issue