Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7388
Title: Relative entropy fuzzy c-means clustering
Authors: Zarinbal, M.
Zarandi, Mohammad Hossein Fazel
Türkşen, İsmail Burhan
Keywords: Fuzzy clustering
Relative entropy
Fuzzy c-means
Relative entropy fuzzy c-means clustering
Issue Date: 2014
Publisher: Elsevier Science Inc
Abstract: Pattern recognition is a collection of computer techniques to classify various observations into different clusters of similar attributes in either supervised or unsupervised manner. Application of fuzzy logic to unsupervised classification or clustering methods has resulted in many wildly used techniques such as fuzzy c-means (FCM) method. However, when the observations are too noisy, the performance of such methods might be reduced. Thus, in this paper, a new fuzzy clustering method based on FCM is presented and the relative entropy is added to its objective function as a regularization function to maximize the dissimilarity between clusters. Several examples are provided to examine the performance of the proposed clustering method. The obtained results show that the proposed method has a very good ability in detecting noises and assignment of suitable membership degrees to the observations. (C) 2013 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.ins.2013.11.004
https://hdl.handle.net/20.500.11851/7388
ISSN: 0020-0255
1872-6291
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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