Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/233511
Authors: 
Wang, Bingling
Li, Yingxing
Härdle, Wolfgang
Year of Publication: 
2021
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2021-003
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.
Subjects: 
clustering
expectiles
asymmetric quadratic loss
image segmentation
JEL: 
C00
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.