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Paper Abstract and Keywords
Presentation 2013-02-22 14:00
A learning clustering method for taste data based on user food preference
Daigo Takano (Future Univ. Hakodate) PRMU2012-171
Abstract (in Japanese) (See Japanese page) 
(in English) We developed a method that converts input data and adjusts clustering parameters to obtain ideal clusters. First, we introduced two measures, the matching accuracy and structure accuracy, that evaluate the difference between ideal clusters and the clusters obtained using the method. Next, a data conversion was introduced as a pre-processing of clustering. Data conversion parameters were adjusted using cluster information added to learning data so that the same clusters as the cluster information are obtained. The cluster information was determined by a user preference. This data conversion could thus solve the problem that conventional clustering algorithms based on a statistical similarity could not always provide ideal clusters for users. Moreover, the clustering parameters were adjusted so as to increase the clustering accuracies. Using the parameters that were derived from the learning data, the clusters for the evaluation data were obtained to satisfy the user's request without any cluster information.
Keyword (in Japanese) (See Japanese page) 
(in English) learning / clustering / data conversion / SOM / function approximation / / /  
Reference Info. IEICE Tech. Rep., vol. 112, no. 441, PRMU2012-171, pp. 191-196, Feb. 2013.
Paper # PRMU2012-171 
Date of Issue 2013-02-14 (PRMU) 
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 PRMU2012-171

Conference Information
Committee PRMU  
Conference Date 2013-02-21 - 2013-02-22 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
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Paper Information
Registration To PRMU 
Conference Code 2013-02-PRMU 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A learning clustering method for taste data based on user food preference 
Sub Title (in English)  
Keyword(1) learning  
Keyword(2) clustering  
Keyword(3) data conversion  
Keyword(4) SOM  
Keyword(5) function approximation  
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1st Author's Name Daigo Takano  
1st Author's Affiliation Future University Hakodate (Future Univ. Hakodate)
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Speaker Author-1 
Date Time 2013-02-22 14:00:00 
Presentation Time 30 minutes 
Registration for PRMU 
Paper # PRMU2012-171 
Volume (vol) vol.112 
Number (no) no.441 
Page pp.191-196 
#Pages 6 
Date of Issue 2013-02-14 (PRMU) 

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