General equalization fuzzy C-means clustering algorithm.

Fuzzy C-means clustering (FCM) is a fast and effective clustering algorithm, but it doesn't consider the difference of the samples size, while there exist great difference in the sample capacities of each class, the decision of FCM will be benificial to the class with less samples. A new clustering algorithm was proposed in the paper and named as general equalization fuzzy C-means clustering (GEFCM), GEFCM modified the minimum cost function of FCM and the factor of samples size was embedded in GEFCM cost function, GEFCM weakened the disturbance of sample size difference to clustering decision. The properties of GEFCM was obtained by theoretical analysis, GFECM breaks the restriction which FCM fuzzy membership can only be the distance analytical solution. The effectiveness and robustness of GEFCM are proved through simulation experiments. [ABSTRACT FROM AUTHOR]