A maximum-entropy approach to fuzzy clustering

In this paper, we propose a new approach to fuzzy clustering by means of a maximum-entropy inference (MEI) method. The resulting formulas have a better form and clearer physical meaning than those obtained by means of the fuzzy c-means (FCM) method. In order to solve the cluster validity problem, we introduce a structure strength function as clustering criterion, which is valid for any membership assignments, thereby being capable of determining the plausible number of clusters according to our subjective requisition. With the proposed structure strength function, we also discuss global minimum problem in terms of simulated annealing. Finally, we simulate a numerical example to demonstrate the approach discussed, and compare our results with those obtained by the traditional approaches