| Literature DB >> 27617051 |
Kean Ming Tan1, Daniela Witten2.
Abstract
In this manuscript, we study the statistical properties of convex clustering. We establish that convex clustering is closely related to single linkage hierarchical clustering and k-means clustering. In addition, we derive the range of the tuning parameter for convex clustering that yields a non-trivial solution. We also provide an unbiased estimator of the degrees of freedom, and provide a finite sample bound for the prediction error for convex clustering. We compare convex clustering to some traditional clustering methods in simulation studies.Entities:
Keywords: Degrees of freedom; fusion penalty; hierarchical clustering; k-means; prediction error; single linkage
Year: 2015 PMID: 27617051 PMCID: PMC5014420 DOI: 10.1214/15-EJS1074
Source DB: PubMed Journal: Electron J Stat ISSN: 1935-7524 Impact factor: 1.125