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K-expectiles clustering

Bingling Wang, Yingxing Li and Wolfgang Karl Härdle

Journal of Multivariate Analysis, 2022, vol. 189, issue C

Abstract: 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 loss, 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. All calculation can be redone via quantlet.com.

Keywords: Adaptive; Asymmetric quadratic loss; Clustering; Expectiles; Functional data; Image segmentation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.jmva.2021.104869

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