Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7605
Title: Scalable model-based cluster analysis using clustering features
Authors: Jin, Huidong 
Prof. LEUNG Kwong Sak 
Wong, Man-Leung 
Xu, Zong-Ben 
Issue Date: 2005
Source: Pattern Recognition, 2005, vol. 38 ( 5), pp. 637 - 649
Journal: Pattern Recognition 
Abstract: 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. © 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7605
ISSN: 00313203
DOI: 10.1016/j.patcog.2004.07.012
Appears in Collections:Applied Data Science - Publication

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