Strong consistency of k-parameters clustering

María Teresa Gallegos and Gunter Ritter

Journal of Multivariate Analysis, 2013, vol. 117, issue C, 14-31

Abstract: Pollard showed for k-means clustering and a very broad class of sampling distributions that the optimal cluster means converge to the solution of the related population criterion as the size of the data set increases. We extend this consistency result to k-parameters clustering, a method derived from the heteroscedastic, elliptical classification model. It allows a more sensitive data analysis and has the advantage of being affine equivariant. Moreover, the present theory yields a consistent criterion for selecting the number of clusters in such models.

Keywords: Cluster analysis; Classification models; Elliptical models; Maximum likelihood estimation; Strong consistency (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (2)

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DOI: 10.1016/j.jmva.2013.01.013

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