Scalable model-based clustering for large databases based on data summarization

Huidong JIN, Man Leung WONG, K.-S. LEUNG

Research output: Journal PublicationsJournal Article (refereed)peer-review

35 Citations (SciVal)


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.
Original languageEnglish
Pages (from-to)1710-1719
Number of pages10
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number11
Publication statusPublished - 1 Jan 2015


  • Scalable clustering; Gaussian mixture model; expectation-maximization; data summary; maximum penalized likelihood estimate


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