Convex clustering via l1 fusion penalization

Summary We study the large sample behaviour of a convex clustering framework, which minimizes the sample within cluster sum of squares under an l1 fusion constraint on the cluster centroids. This recently proposed approach has been gaining in popularity; however, its asymptotic properties have remai...

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Veröffentlicht in:	Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2017-11, Vol.79 (5), p.1527-1546
Hauptverfasser:	Radchenko, Peter, Mukherjee, Gourab
Format:	Artikel
Sprache:	eng
Schlagworte:	Asymptotic properties Cardinality Centroids Clustering Clusters Convergence Convex clustering Fusion penalties Number of clusters Popularity Post-production processing Rates of convergence Regression analysis Statistical methods Statistics Virology
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