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Scalable model-based cluster analysis using clustering features
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DOI：10.1016/j.patcog.2004.07.012.png)
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Abstract
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We present two scalable model-based clustering systems based on a Gaussian mixture model with independent attributes within clusters. They first summarize data into sub-clusters, and then generate Gaussian mixtures from their clustering features using a new algorithm-EMACF. EMACF approximates the aggregate behavior of each sub-cluster of data items in the Gaussian mixture model. It provably converges. The experiments show that our clustering systems run one or two orders of magnitude faster than the traditional EM algorithm with few losses of accuracy. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
cluster analysis
data mining
scalable
Gaussian mixture model
expectation maximization
clustering feature
convergence
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