Tsallis Entropy Based Fuzzy C-means Clustering with Parameter Adjustment

This article is dealing with the fuzzy clustering method which combines the deterministic annealing (DA) approach with Tsallis entropy. Tsallis entropy is a q parameter extension of Shannon entropy. By maximizing Tsallis entropy within the framework of fuzzy c-means (FCM), a membership function similar to the statistical mechanical distribution functions is obtained. One of the major issue of the Tsallis entropy maximization method is that how to determine the q value is not clear. We have adjusted the q value to minimize the objective function, because q strongly affects the extent of the membership function. Numerical experiments are performed and the obtained results indicate that the proposed method works properly and the q value can be adjusted so as to make a membership function fit to a data distribution.