Dynamic clustering via asymptotics of the dependent Dirichlet process mixture
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Low_dynamic clustering.pdf
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2.81 MB
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Adobe PDF
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Author(s)
Campbell, Trevor David
Liu, Miao
Kulis, Brian
How, Jonathan P.
Carin, Lawrence
Date Issued
2013
Journal
Advances in Neural Information Processing Systems (NIPS) 26
Publisher
Neural Information Processing Systems Foundation
Citation
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.
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
http://papers.nips.cc/paper/5094-dynamic-clustering-via-asymptotics-of-the-dependent-dirichlet-process-mixture
