Skip to main content
Article
Scalable model-based cluster analysis using clustering features
Pattern Recognition
  • Huidong JIN, CSIRO Data Mining Research, Australia
  • Kwong Sak LEUNG, Chinese University of Hong Kong
  • Man Leung WONG, Lingnan University, Hong Kong
  • Zong Ben XU, Xi'An Jiaotong University, China
Document Type
Journal article
Publication Date
5-1-2005
Keywords
  • Cluster analysis,
  • Clustering feature,
  • Convergence,
  • Data mining,
  • Expectation maximization,
  • Gaussian mixture model,
  • Scalable
Disciplines
Abstract

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.

DOI
10.1016/j.patcog.2004.07.012
E-ISSN
18735142
Publisher Statement

Copyright © 2005 Pattern Recognition Society

Access to external full text or publisher's version may require subscription.

Full-text Version
Publisher’s Version
Citation Information
Jin, H., Leung, K.-S., Wong, M.-L., & Wu, Z.-B. (2005). Scalable model-based cluster analysis using clustering features. Pattern Recognition, 38(5), 637-649. doi: 10.1016/j.patcog.2004.07.012