Clustering with Bregman Divergences: an Asymptotic Analysis
Clustering, in particular $k$ means clustering, is a central topic in data analysis. Clustering with Bregman divergences is a recently proposed generalization of $k$ means clustering which has already been widely used in applications. In this paper we analyze theoretical properties of Bregman clustering when the number of the clusters $k$ is large. We establish quantization rates and describe the limiting distribution of the centers as $k\to \infty$ extending well known results for $k$ means clustering.
NIPS