Generalized noise clustering as a robust fuzzy c-M-estimators model

R.N. Dave´s (1991) noise clustering (NC) algorithm has been generalized in an earlier work where the noise distance δ is allowed to take different values for different feature vectors. Based on that, it was shown that the membership generated by the NC algorithm is a product of two terms, one is the original fuzzy c-means (FCM) membership responsible for data partitioning and the other is a generalized possibilistic membership that achieves a mode seeking effect, and imparts robustness. It is shown that a variety of robust M-estimators can be incorporated into the generalized NC algorithm, for example Huber, Hampel, Cauchy, Tukey biweight, and Andrew´s sine. The generalized NC algorithm is also compared with the recently introduced mixed c-means and a noise resistant FCM technique