Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7623
Title: Scalable model-based clustering by working on data summaries
Authors: Jin, Huidong 
Wong, Man-Leung 
Prof. LEUNG Kwong Sak 
Issue Date: 2003
Source: Proceedings - IEEE International Conference on Data Mining, 2003, ICDM, pp. 91 - 98
Journal: Proceedings - IEEE International Conference on Data Mining, ICDM 
Abstract: The scalability problem in data mining involves the development of methods for handling large databases with limited computational resources. In this paper, we present a two-phase scalable model-based clustering framework: First, a large data set is summed up into sub-clusters; Then, clusters are directly generated from the summary statistics of sub-clusters by a specifically designed Expectation-Maximization (EM) algorithm. Taking example for Gaussian mixture models, we establish a provably convergent EM algorithm, EMADS, which embodies cardinality, mean, and covariance information of each sub-cluster explicitly. Combining with different data summarization procedures, EMADS is used to construct two clustering systems: gEMADS and bEMADS. The experimental results demonstrate that they run several orders of magnitude faster than the classic EM algorithm with little loss of accuracy. They generate significantly better results than other model-based clustering systems using similar computational resources. © 2003 IEEE.
Type: Conference Proceedings
URI: http://hdl.handle.net/20.500.11861/7623
ISBN: 0769519784
978-076951978-4
ISSN: 15504786
Appears in Collections:Applied Data Science - Publication

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