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Paper Abstract and Keywords
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
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

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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