Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7387
Title: Relative entropy collaborative fuzzy clustering method
Authors: Zarinbal, M.
Zarandi, Mohammad Hossein Fazel
Türkşen, İsmail Burhan
Keywords: Fuzzy set theory
Horizontal and vertical collaborative fuzzy clustering
Relative entropy
Relative entropy collaborative fuzzy clustering
Issue Date: 2015
Publisher: Elsevier Sci Ltd
Abstract: The main task of clustering methods, especially fuzzy methods, is to find whether natural grouping exists in data and to impose identity on them. In some situations, data are stored in several data sites and to discover the global structures, clustering methods have to be aware of dependencies in all data sites. Collaborative fuzzy clustering methods have been proposed and widely studied to answer such need. In this paper, a novel collaborative fuzzy clustering method is proposed. In this method, relative entropy concept is used as the communication method, a new approach is applied to calculate the interaction coefficient between data sites, and horizontal and vertical modes of the proposed method are discussed. Performance of the proposed method is evaluated using several experiments and the results show that it has the highest quality of collaboration and could classify data more efficiently. (C) 2014 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.patcog.2014.09.018
https://hdl.handle.net/20.500.11851/7387
ISSN: 0031-3203
1873-5142
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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