Maintaining Gaussian Mixture Models of Data Streams Under Block Evolution

  • J. P. Patist
  • W. Kowalczyk
  • E. Marchiori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)


A new method for maintaining a Gaussian mixture model of a data stream that arrives in blocks is presented. The method constructs local Gaussian mixtures for each block of data and iteratively merges pairs of closest components. Time and space complexity analysis of the presented approach demonstrates that it is 1-2 orders of magnitude more efficient than the standard EM algorithm, both in terms of required memory and runtime.


Data Stream Gaussian Mixture Model Expectation Maximization Algorithm Compression Rate Greedy Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. P. Patist
    • 1
  • W. Kowalczyk
    • 1
  • E. Marchiori
    • 1
  1. 1.Department of Computer ScienceFree University of AmsterdamAmsterdamThe Netherlands

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