Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7597
Title: Scalable model-based clustering for large databases based on data summarization
Authors: Jin, Huidong 
Wong, Man-Leung 
Prof. LEUNG Kwong Sak 
Issue Date: 2005
Source: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, vol. 27 (11), pp. 1710 - 1719
Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence 
Abstract: The scalability problem in data mining involves the development of methods for handling large databases with limited computational resources such as memory and computation time. In this paper, two scalable clustering algorithms, bEMADS and gEMADS, are presented based on the Gaussian mixture model. Both summarize data into subclusters and then generate Gaussian mixtures from their data summaries. Their core algorithm, EMADS, is defined on data summaries and approximates the aggregate behavior of each subcluster of data under the Gaussian mixture model. EMADS is provably convergent. Experimental results substantiate that both algorithms can run several orders of magnitude faster than expectation-maximization with little loss of accuracy © 2005 IEEE.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7597
ISSN: 01628828
DOI: 10.1109/TPAMI.2005.226
Appears in Collections:Applied Data Science - Publication

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