Dynamic clustering via asymptotics of the dependent Dirichlet process mixture
Author(s)Campbell, Trevor David; Liu, Miao; Kulis, Brian; How, Jonathan P.; Carin, Lawrence
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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.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics
Advances in Neural Information Processing Systems (NIPS) 26
Neural Information Processing Systems Foundation
Campbell, Trevor, Miao Liu, Brian Kulis, Jonathan P. How, and Lawrence Carin. "Dynamic clustering via asymptotics of the dependent Dirichlet process mixture." Advances in Neural Information Processing Systems (NIPS) 26, 2013.
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