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Poster
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
Trevor Campbell · Miao Liu · Brian Kulis · Jonathan How · Lawrence Carin

Sat Dec 07 07:00 PM -- 11:59 PM (PST) @ Harrah's Special Events Center, 2nd Floor
in Poster Session »

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. Empirical results from a synthetic test with moving Gaussian clusters and a test with real ADS-B aircraft trajectory data demonstrate that the algorithm requires orders of magnitude less computational time than contemporary probabilistic and hard clustering algorithms, while providing higher accuracy on the examined datasets.

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Author Information

Trevor Campbell (UBC)
Miao Liu (Duke University)
Brian Kulis (Boston University)
Jonathan How (MIT)
Lawrence Carin (KAUST)

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