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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 2324-2347 |
| Number of pages | 24 |
| Journal | Electronic Journal of Statistics |
| Volume | 9 |
| Issue number | 2 |
| DOIs | |
| State | Published - Aug 19 2015 |
Bibliographical note
Publisher Copyright:© 2015, Institute of Mathematical Statistics. All rights reserved.
Keywords
- Degrees of freedom
- Fusion penalty
- Hierarchical clustering
- K-means
- Prediction error
- Single linkage