 Clustering with the Average Silhouette WidthFatima Batool and Christian Hennig Computational Statistics & Data Analysis, 2021, vol. 158, issue C Abstract: The Average Silhouette Width (ASW) is a popular cluster validation index to estimate the number of clusters. The question whether it also is suitable as a general objective function to be optimized for finding a clustering is addressed. Two algorithms (the standard version OSil and a fast version FOSil) are proposed, and they are compared with existing clustering methods in an extensive simulation study covering known and unknown numbers of clusters. Real data sets are analysed, partly exploring the use of the new methods with non-Euclidean distances. The ASW is shown to satisfy some axioms that have been proposed for cluster quality functions. The new methods prove useful and sensible in many cases, but some weaknesses are also highlighted. These also concern the use of the ASW for estimating the number of clusters together with other methods, which is of general interest due to the popularity of the ASW for this task. Keywords: Axiomatic clustering; Distance-based clustering; Partitioning around medoids; Number of clusters (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:158:y:2021:i:c:s0167947321000244 DOI: 10.1016/j.csda.2021.107190 Access Statistics for this article Computational Statistics & Data Analysis is currently edited by S.P. Azen More articles in Computational Statistics & Data Analysis from Elsevier |
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