Maintaining Gaussian Mixture Models of Data Streams Under Block Evolution

  • J. P. Patist
  • W. Kowalczyk
  • E. Marchiori
Conference paper

DOI: 10.1007/11758501_175

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)
Cite this paper as:
Patist J.P., Kowalczyk W., Marchiori E. (2006) Maintaining Gaussian Mixture Models of Data Streams Under Block Evolution. In: Alexandrov V.N., van Albada G.D., Sloot P.M.A., Dongarra J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3991. Springer, Berlin, Heidelberg

Abstract

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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

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

Personalised recommendations