Abstract
There are probabilistic restrictions on traditional Fuzzy c-Means methods which identify membership functions with the sum of membership values at each element as one. On the other hand possibilistic clustering methods identify the membership functions without such a constraint, but the shapes of membership functions are independent from the clusters estimated through the possibilistic methods. In this paper, we proposed FCM based on evidenctial theory. Using our method, we can obtain non-additive membership functions, and their shapes depend on the data distribution, which means that they differ from each other. To show the feasibility of the proposed method we have carried out some numerical experiments.
