Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
neural information processing systems, Volume abs/1305.6659, 2013, Pages 449-457.
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 wit...More
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