Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6110
Title: A new credibilistic clustering
Authors: Kalhori, M. Rostam Niakan
Zarandi, Mohammad Hossein Fazel
Türkşen, İsmail Burhan
Keywords: Credibilistic clustering
Credibility measure
Objective function-based clustering
Issue Date: 2014
Publisher: Elsevier Science Inc
Abstract: This paper focuses on credibilistic clustering approach. A data clustering method partitions unlabeled data sets into clusters and labels them for various goals such as computer vision and pattern recognition. There are different models for objective function-based fuzzy clustering such as Fuzzy C-Means (FCM), Possibilistic C-Mean (PCM) and their combinations. Credibilistic clustering is a new approach in this field. In this paper, a new credibilistic clustering model is introduced in which credibility measure is applied instead of possibility measure in possibilistic clustering. Also, in objective function, the separation of clusters is considered in addition to the compactness within clusters. The steps of clustering are designed based on this approach. Finally, the main issues about model are discussed, and the results of computational experiments are presented to show the efficiency of the proposed model. (C) 2014 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.ins.2014.03.106
https://hdl.handle.net/20.500.11851/6110
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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