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| Presentation | 2012-09-02 10:00 Information theoretic clustering using competitive learning -- Comparison of criterion functions and algorithms for document clustering -- Toshio Uchiyama (NTT)  PRMU2012-33 IBISML2012-16 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Information-theoretic clustering (ITC) finds clusters based on the similarity of the distributions of features. An ITC algorithm based on optimizing the clustering criterion has previously been proposed. This algorithm is reminiscent of the k-means algorithm, but uses Kullback-Leibler (KL) divergence when updating the cluster-labels of the data. Recently, a novel method, based on the idea above, has been proposed. It uses competitive learning, which is known to be superior to the k-means algorithm. The method also uses skew divergence instead of KL divergence to avoid the zero-frequency problem. This paper shows that the method performs better than existing clustering algorithms, such as maximum margin clustering and a method based on mixture of von Mises-Fisher distribution, when applied to text data sets in multiclass problems. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Information-theoretic clustering / Competitive learning / Skew divergence / Kullback-Leibler divergence / Entropy / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 112, no. 198, IBISML2012-16, pp. 23-30, Sept. 2012. | |
| Paper # | IBISML2012-16 | |
| Date of Issue | 2012-08-26 (PRMU, IBISML) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | PRMU2012-33 IBISML2012-16 |
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| Conference Information | |
| Committee | PRMU IBISML IPSJ-CVIM |
| Conference Date | 2012-09-02 - 2012-09-03 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2012-09-PRMU-IBISML-CVIM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Information theoretic clustering using competitive learning |
| Sub Title (in English) | Comparison of criterion functions and algorithms for document clustering |
| Keyword(1) | Information-theoretic clustering |
| Keyword(2) | Competitive learning |
| Keyword(3) | Skew divergence |
| Keyword(4) | Kullback-Leibler divergence |
| Keyword(5) | Entropy |
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| 1st Author's Name | Toshio Uchiyama |
| 1st Author's Affiliation | Nippon Telegraph and Telephone corporation (NTT) |
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| Speaker | Author-1 |
| Date Time | 2012-09-02 10:00:00 |
| Presentation Time | 30 minutes |
| Registration for | IBISML |
| Paper # | PRMU2012-33, IBISML2012-16 |
| Volume (vol) | vol.112 |
| Number (no) | no.197(PRMU), no.198(IBISML) |
| Page | pp.23-30 |
| #Pages | 8 |
| Date of Issue | 2012-08-26 (PRMU, IBISML) |
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