M-Estimator induced Fuzzy Clustering Algorithms

M-estimators can be seen as a special case of robust clustering algorithms. In this paper, we present the reversed direction and show that clustering algorithms can be constructed by using M-estimators. A clever normalization is used to link the values of several M-estimator prototypes together in one clustering algorithm. A variety of M-estimators and several normalization strategies are used in 4 data sets to present their dierences and properties. The results are evaluated using 5 dierent clustering validation indices.