Journal
ELECTRONIC JOURNAL OF STATISTICS
Volume 9, Issue 2, Pages 2324-2347Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/15-EJS1074
Keywords
Degrees of freedom; fusion penalty; hierarchical clustering; k-means; prediction error; single linkage
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Funding
- Sloan Research Fellowship
- NIH [DP5OD009145]
- NSF CAREER [DMS-1252624]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1252624] Funding Source: National Science Foundation
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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.
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