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.

Clustering with the Average Silhouette Width

Hennig C.
2021

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/848943
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