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Title: Interval Type-2 Relative Entropy Fuzzy C-Means clustering
Authors: Zarinbal, M.
Zarandi, Mohammad Hossein Fazel
Türkşen, İsmail Burhan
Keywords: Interval Type-2 fuzzy set theory
Interval arithmetic
Relative entropy
Fuzzy c-means clustering
Interval Type-2 Relative Entropy Fuzzy
C-Means clustering
Issue Date: 2014
Publisher: Elsevier Science Inc
Abstract: Fuzzy set theory especially Type-2 fuzzy set theory provides an efficient tool for handling uncertainties and vagueness in real world observations. Among various clustering techniques, Type-2 fuzzy clustering methods are the most effective methods in the case of having no prior knowledge about observations. While uncertainties in Type-2 fuzzy clustering parameters are investigated by researchers, uncertainties associated with membership degrees are not very well discussed in the literature. In this paper, investigating the latter uncertainties is our concern and Interval Type-2 Relative Entropy Fuzzy C-Means (IT2 REFCM) clustering method is proposed. The computational complexity of the proposed method is discussed and its performance is examined using several experiments. The obtained results show that the proposed method has a very good ability in detecting noises and assignment of suitable membership degrees to observations. (C) 2014 Elsevier Inc. All rights reserved.
ISSN: 0020-0255
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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