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Proceedings Paper

Highly efficient incremental estimation of Gaussian mixture models for online data stream clustering
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Paper Abstract

We present a probability-density-based data stream clustering approach which requires only the newly arrived data, not the entire historical data, to be saved in memory. This approach incrementally updates the density estimate taking only the newly arrived data and the previously estimated density. The idea roots on a theorem of estimator updating and it works naturally with Gaussian mixture models. We implement it through the expectation maximization algorithm and a cluster merging strategy by multivariate statistical tests for equality of covariance and mean. Our approach is highly efficient in clustering voluminous online data streams when compared to the standard EM algorithm. We demonstrate the performance of our algorithm on clustering a simulated Gaussian mixture data stream and clustering real noisy spike signals extracted from neuronal recordings.

Paper Details

Date Published: 28 March 2005
PDF: 10 pages
Proc. SPIE 5803, Intelligent Computing: Theory and Applications III, (28 March 2005); doi: 10.1117/12.601724
Show Author Affiliations
Mingzhou Song, Queens College/CUNY (United States)
Graduate Ctr./CUNY (United States)
Hongbin Wang, Graduate Ctr./CUNY (United States)

Published in SPIE Proceedings Vol. 5803:
Intelligent Computing: Theory and Applications III
Kevin L. Priddy, Editor(s)

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