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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 #None

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.

Author Information

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

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