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Scalable model-based clustering for large databases based on data summarization
IEEE Transactions on Pattern Analysis & Machine Intelligence
  • Huidong JIN
  • Man Leung WONG, Lingnan University, Hong Kong
  • K.-S. LEUNG
Document Type
Journal article
Publication Date
  • Scalable clustering; Gaussian mixture model; expectation-maximization; data summary; maximum penalized likelihood estimate

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.

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Copyright © 2005 Institute of Electrical and Electronics Engineers.

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Citation Information
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