Scalable model-based cluster analysis using clustering features

Huidong JIN, Kwong Sak LEUNG, Man Leung WONG, Zong Ben XU

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

20 Citations (Scopus)


We present two scalable model-based clustering systems based on a Gaussian mixture model with independent attributes within clusters. They first summarize data into sub-clusters, and then generate Gaussian mixtures from their clustering features using a new algorithm - EMACF. EMACF approximates the aggregate behavior of each sub-cluster of data items in the Gaussian mixture model. It provably converges. The experiments show that our clustering systems run one or two orders of magnitude faster than the traditional EM algorithm with few losses of accuracy.
Original languageEnglish
Pages (from-to)637-649
Number of pages13
JournalPattern Recognition
Issue number5
Publication statusPublished - 1 May 2005


  • Cluster analysis
  • Clustering feature
  • Convergence
  • Data mining
  • Expectation maximization
  • Gaussian mixture model
  • Scalable


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