Supervised fuzzy partitioning

Pooya Ashtari, Fateme Nateghi Haredasht, Hamid Beigy

Abstract: Highlights•A framework is presented, extending k-means clustering to be applicable to supervised tasks.•We adopt an entropy approach to fuzzification and feature weighting.•An efficient block coordinate descent scheme is formulated to find local minima.•A flexible, nonlinear classifier is presented, capable of handling high-dimensional settings.•Experimental results show the superior performance of the proposed method over state-of-the-art classifiers.