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Dynamic clustering via asymptotics of the dependent Dirichlet process mixture

Publication ,  Journal Article
Campbell, T; Liu, M; Kulis, B; How, JP; Carin, L
Published in: Advances in Neural Information Processing Systems
January 1, 2013

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 lowvariance 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.

Duke Scholars

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2013

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology
 

Citation

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MLA
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Campbell, T., Liu, M., Kulis, B., How, J. P., & Carin, L. (2013). Dynamic clustering via asymptotics of the dependent Dirichlet process mixture. Advances in Neural Information Processing Systems.
Campbell, T., M. Liu, B. Kulis, J. P. How, and L. Carin. “Dynamic clustering via asymptotics of the dependent Dirichlet process mixture.” Advances in Neural Information Processing Systems, January 1, 2013.
Campbell T, Liu M, Kulis B, How JP, Carin L. Dynamic clustering via asymptotics of the dependent Dirichlet process mixture. Advances in Neural Information Processing Systems. 2013 Jan 1;
Campbell, T., et al. “Dynamic clustering via asymptotics of the dependent Dirichlet process mixture.” Advances in Neural Information Processing Systems, Jan. 2013.
Campbell T, Liu M, Kulis B, How JP, Carin L. Dynamic clustering via asymptotics of the dependent Dirichlet process mixture. Advances in Neural Information Processing Systems. 2013 Jan 1;

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2013

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology