| Presentation | 2003/9/8 A Graph-Based Approach for Temporal Relationship Mining Ryutaro ICHISE, Masayuki NUMAO, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| 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. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Machine learning / Temporal mining / Active mining / Inductive Logic Programming |
| Paper # | AI2003-52 |
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| Conference Information | |
| Committee | AI |
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| Conference Date | 2003/9/8(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Artificial Intelligence and Knowledge-Based Processing (AI) |
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| 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 | |