Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7756
Title: Type-II Fuzzy Possibilistic C-Mean Clustering
Authors: Zarandi, Mohammad Hossein Fazel
Zarinbal, M.
Türkşen, İsmail Burhan
Keywords: Type-II Fuzzy Logic
Possibilistic C-Mean (PCM)
Mahalanobis Distance
Cluster Validity Index
Issue Date: 2009
Publisher: European Soc Fuzzy Logic & Technology
Source: Joint World Congress of International-Fuzzy-Systems-Association (IFSA)/European Conference of European-Society-for-Fuzzy-Logic-and-Technology (EUSFLAT) -- JUL 20-24, 2009 -- Lisbon, PORTUGAL
Abstract: Fuzzy clustering is well known as a robust and efficient way to reduce computation cost to obtain the better results. In the literature, many robust fuzzy clustering models have been presented such as Fuzzy C-Mean (FCM) and Possibilistic C-Mean (PCM), where these methods are Type-I Fuzzy clustering. Type-II Fuzzy sets, on the other hand, can provide better performance than Type-I Fuzzy sets, especially when many uncertainties are presented in real data. The focus of this paper is to design a new Type-II Fuzzy clustering method based on Krishnapuram and Keller PCM. The proposed method is capable to cluster Type-II fuzzy data and can obtain the better number of clusters (c) and degree of fuzziness (m) by using Type-II Kwon validity index. In the proposed method, two kind of distance measurements, Euclidean and Mahalanobis are examined. The results show that the proposed model, which uses Mahalanobis distance based on Gustafson and Kessel approach is more accurate and can efficiently handle uncertainties.
URI: https://hdl.handle.net/20.500.11851/7756
ISBN: 978-989-95079-6-8
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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