K-expectiles clustering

Bingling Wang, Yingxing Li and Wolfgang Härdle

No 2021-003, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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 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: clustering; expectiles; asymmetric quadratic loss; image segmentation (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-cmp, nep-ecm, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Journal Article: K-expectiles clustering (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2021003

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