An Improved Clustering Algorithm for Information Granulation
 Qinghua Hu,
 Daren Yu
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Abstract
Cmeans clustering is a popular technique to classify unlabeled data into different categories. Hard cmeans (HCM), fuzzy cmeans (FCM) and rough cmeans (RCM) were proposed for various applications. In this paper a fuzzy rough cmeans algorithm (FRCM) is present, which integrates the advantage of fuzzy set theory and rough set theory. Each cluster is represented by a center, a crisp lower approximation and a fuzzy boundary. The Area of a lower approximation is controlled over a threshold T, which also influences the fuzziness of the final partition. The analysis shows the proposed FRCM achieves the tradeoff between convergence and speed relative to HCM and FCM. FRCM will degrade to HCM or FCM by changing the parameter T. One of the advantages of the proposed algorithm is that the membership of clustering results coincides with human’s perceptions, which makes the method has a potential application in understandable fuzzy information granulation.
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 Title
 An Improved Clustering Algorithm for Information Granulation
 Book Title
 Fuzzy Systems and Knowledge Discovery
 Book Subtitle
 Second International Conference, FSKD 2005, Changsha, China, August 2729, 2005, Proceedings, Part I
 Pages
 pp 494504
 Copyright
 2005
 DOI
 10.1007/11539506_63
 Print ISBN
 9783540283126
 Online ISBN
 9783540318309
 Series Title
 Lecture Notes in Computer Science
 Series Volume
 3613
 Series ISSN
 03029743
 Publisher
 Springer Berlin Heidelberg
 Copyright Holder
 SpringerVerlag Berlin Heidelberg
 Additional Links
 Topics
 Industry Sectors
 eBook Packages
 Editors

 Lipo Wang ^{(18)}
 Yaochu Jin ^{(19)}
 Editor Affiliations

 18. School of Electrical and Electronic Engineering, Nanyang Technological University
 19. Honda Research Institute Europe GmbH, Offenbach/Main
 Authors

 Qinghua Hu ^{(20)}
 Daren Yu ^{(20)}
 Author Affiliations

 20. Harbin Institute of Technology, Harbin, 150001, China
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