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Issue title: Recent Advances in Soft Computing: Theories and Applications
Article type: Research Article
Authors: Effati, S. | Yazdi, H. Sadoghi | Sharahi, A. Jiryani
Affiliations: Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran | Department of Mathematics, Sabzevar Tarbiat Moallem University, Sabzevar, Iran
Note: [] Corresponding author. S. Effati, Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran. E-mails: [email protected] (S. Effati), [email protected] (H. Sadoghi), [email protected] (A. Jiryani).
Abstract: This paper presents fuzzy clustering algorithm for fuzzy data based on α-cuts. A new suitable definition for distance between two arbitrary fuzzy numbers based on α-cuts is proposed. We then reformulate fuzzy c-means (FCM) with fuzzy data and fuzzy centers based on α-cuts. The effectiveness of the proposed clustering algorithm is tested for three fuzzy data sets and then it is compared with other methods; the fuzzy c-number (FCN) algorithm, Hathaway's FCM algorithm and the mixed-type variables FCM (MVFCM) algorithm.
Keywords: α-cut, fuzzy numbers, fuzzy c-means, fuzzy clustering, fuzzy data, dissimilarity measure
DOI: 10.3233/IFS-2012-0528
Journal: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 3, pp. 511-519, 2013
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