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| Presentation | 2022-01-18 15:20 Determining the number of clusters using the shrinking maximum likelihood self-organizing map Ryosuke Motegi, Yoichi Seki (Gunma Univ.)  IBISML2021-29 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Determining the number of clusters is one of the major challenges in clustering. The conventional method, such as the Expectation-Maximization (EM) algorithm, determines the number of clusters by comparing the estimated models for each cluster independently, and the initial value dependency of the estimation method and the computational cost are issues. This study proposes a method to efficiently estimate the appropriate number of clusters with less initial value dependency. The proposed method constructs clusters based on the Self-Organizing Map (SOM) learning rule and searches for the number of clusters by repeating the procedure of updating the cluster structure based on the fit of the clusters to the data. Using artificial data, we show that the SOM learning rule can reduce the initial value dependency, and our method can efficiently search for the appropriate number of clusters. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | self-organizing map / model-based clustering / model selection / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 321, IBISML2021-29, pp. 81-87, Jan. 2022. | |
| Paper # | IBISML2021-29 | |
| Date of Issue | 2022-01-10 (IBISML) | |
| ISSN | 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 | IBISML2021-29 |
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| Conference Information | |
| Committee | IBISML |
| Conference Date | 2022-01-17 - 2022-01-18 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Machine Learning, etc. |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2022-01-IBISML |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Determining the number of clusters using the shrinking maximum likelihood self-organizing map |
| Sub Title (in English) | |
| Keyword(1) | self-organizing map |
| Keyword(2) | model-based clustering |
| Keyword(3) | model selection |
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| 1st Author's Name | Ryosuke Motegi |
| 1st Author's Affiliation | Gunma University (Gunma Univ.) |
| 2nd Author's Name | Yoichi Seki |
| 2nd Author's Affiliation | Gunma University (Gunma Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-01-18 15:20:00 |
| Presentation Time | 20 minutes |
| Registration for | IBISML |
| Paper # | IBISML2021-29 |
| Volume (vol) | vol.121 |
| Number (no) | no.321 |
| Page | pp.81-87 |
| #Pages | 7 |
| Date of Issue | 2022-01-10 (IBISML) |
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