Derivatives of Fuzzy C-means method and their application comparisons

Fuzzy C-means (FCM) is also called soft K-means, which is a wildly used unsupervised clustering method. Its derivatives comes out for different requirements, in this paper we compare four related clustering algorithms, which includes FCM and traditional Relational Fuzzy C-means (RFCM) and None Euclidean Relational Fuzzy C-means (NERFCM) and Any Relational Fuzzy C-means (ARFCM). Their common points and different limitations on usage are discussed, finally an optimal clustering algorithm is chosen for application on human posture classification, and experiments prove its efficiency and sensitivity.