A Fuzzy c-means Algorithm Based on an Adaptive L2 Minkowsky Distance

An extension of the fuzzy c-means clustering algorithm based on an adaptive distance is presented. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion based on an adaptive L2 Minkowsky distance that changes at each algorithm’s iteration. Experiments with real and synthetic data sets show the usefulness of this method.