| Presentation | 1998/5/21 A Study of Bayesian Clustering of Document Set Based on GA Keiko AOKI, Kazunori MATSUMOTO, Keiichiro HOASHI, Kazuo HASHIMOTO, |
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| PDF Download Page | PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In order to reduce calculations in Bayesian clustering of document set, we introduce an algorithm to decide a semi-optimal cluster by GA, whose genes encode cluster structures. The proposed method uses Minimum Description Length (MDL) of clusters for the evaluation of candidate genes. We compare the performance of the proposed algorithm with those of a conventional Bayesian clustering algorithm and a semi-optimal algorithm known as"topdown clustering". The empirical result shows the accuracy of proposed method for document retrieval is better than that of topdown clustering. It proves the effectiveness of a search based on GA. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Text / Bayesian Clustering / GA / Hispeed |
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| Conference Information | |
| Committee | AI |
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| Conference Date | 1998/5/21(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 | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A Study of Bayesian Clustering of Document Set Based on GA |
| Sub Title (in English) | |
| Keyword(1) | Text |
| Keyword(2) | Bayesian Clustering |
| Keyword(3) | GA |
| Keyword(4) | Hispeed |
| 1st Author's Name | Keiko AOKI |
| 1st Author's Affiliation | KDD R&D Laboratories() |
| 2nd Author's Name | Kazunori MATSUMOTO |
| 2nd Author's Affiliation | KDD R&D Laboratories |
| 3rd Author's Name | Keiichiro HOASHI |
| 3rd Author's Affiliation | KDD R&D Laboratories |
| 4th Author's Name | Kazuo HASHIMOTO |
| 4th Author's Affiliation | KDD R&D Laboratories |
| Date | 1998/5/21 |
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| Volume (vol) | vol.98 |
| Number (no) | 58 |
| Page | pp.pp.- |
| #Pages | 7 |
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