Convergence properties of the generalized fuzzy c-means clustering algorithms

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Fuzzy c-means (FCM) clustering algorithms have been widely used to solve clustering problems. Yang and Yu [1] extended these to optimization procedures with respect to any probability distribution. They showed that the optimal cluster centers are the fixed points of these generalized FCM clustering algorithms. The convergence properties of algorithms are the important theoretical issue. In this paper, we present convergence properties of the generalized FCM clustering algorithms. These are global convergence, local convergence, and its rate of convergence.