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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 |
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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