Scalable model-based clustering for large databases based on data summarization
Document Type
Journal article
Source Publication
IEEE Transactions on Pattern Analysis & Machine Intelligence
Publication Date
2015
Volume
27
Issue
11
First Page
1710
Last Page
1719
Keywords
Scalable clustering; Gaussian mixture model; expectation-maximization; data summary; maximum penalized likelihood estimate
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.
DOI
10.1109/TPAMI.2005.226
Print ISSN
01628828
E-ISSN
19393539
Publisher Statement
Copyright © 2005 Institute of Electrical and Electronics Engineers.
Access to external full text or publisher's version may require subscription.
Full-text Version
Publisher’s Version
Language
English
Recommended Citation
Jin, H., Wong, M.-L., & Leung, K.-S. (2005). Scalable model-based clustering for large databases based on data summarization. IEEE Transactions on Pattern Analysis & Machine Intelligence, 27(11), 1710-1719. doi: 10.1109/TPAMI.2005.226