K-expectiles clustering - Bingling Wang; Yingxing Li; Wolfgang Härdle (Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", 2021)
K-means clustering is one of the most widely-used partitioning algorithm in cluster analysis due to its simplicity and computational efficiency, but it may not provide ideal clustering results when applying to data with non-spherically shaped clusters. By considering the asymmetrically weighted distance, We propose the K-expectile clustering and search the clusters via a greedy algorithm that minimizes the within cluster τ -variance. We provide algorithms based on two schemes: the fixed τ clustering, and the adaptive τ clustering. Validated by simulation results, our method has enhanced performance on data with asymmetric shaped clusters or clusters with a complicated structure. Applications of our method show that the fixed τ clustering can bring some flexibility on segmentation with a decent accuracy, while the adaptive τ clustering may yield better performance.
Keywords: C00; 330; clustering; expectiles; asymmetric quadratic loss; image segmentation