Parameter selection for suppressed fuzzy c-means clustering algorithm based on fuzzy partition entropy

Suppressed fuzzy c-means (S-FCM) clustering algorithm with the intention of combining the higher speed of hard c-means clustering algorithm and the better classification performance of fuzzy c-means clustering algorithm had been studied by many researchers and applied in many fields. In this algorithm, the parameter selection is very important on the algorithm performance. Huang proposed a modified S-FCM, named as MS-FCM, to determine the parameter α with type-driven learning. α is updated each iteration and successful used in MRI segmentation. In this paper, we give another method to select the parameter α based on the fuzzy partition entropy. Numerical examples will serve to illustrate the effectiveness of proposed algorithm.