Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture

NeurIPS 2013 Trevor CampbellMiao LiuBrian KulisJonathan P. HowLawrence Carin

This paper presents a novel algorithm, based upon the dependent Dirichlet process mixture model (DDPMM), for clustering batch-sequential data containing an unknown number of evolving clusters. The algorithm is derived via a low-variance asymptotic analysis of the Gibbs sampling algorithm for the DDPMM, and provides a hard clustering with convergence guarantees similar to those of the k-means algorithm... (read more)

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