Fuzzy K-expectiles clustering

In this paper the Fuzzy K-expectiles clustering model is proposed. The model takes into account the asymmetry inherent in the data distribution, extending its applicability to a broader spectrum of data than the Fuzzy K-means. To achieve this, the Fuzzy K-expectiles clustering model introduces the cluster centroid expectile, and assigns data points based on expectile distances. An adaptive asymmetry parameter is specified for each variable and for each cluster The performance of the adaptive Fuzzy K-expectiles model is compared with other clustering models suggested in the literature. To show the performances of the proposed model three simulation studies and three applications to real datasets are presented.