Adapted Mean Variable Distance to Fuzzy-Cmeans for Effective Image Clustering

C-means had been used for data clustering problems for recently years. However, if it uses the non-robust objective function of FCM (Fuzzy C-Means), we will get poor result if data corrupted because some noises. To improve these problems, this paper make effective objective functions of Fuzzy C-means which named MVDFCM (Mean Variable Distance Fuzzy C-means). The method is with center learning method which is on the basis of quadratic mean distance, entropy methods, and regularization terms. Moreover, the center learning method can cut down the computation complexity and running time. The results show the proposed method get more quality to the previous method.