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International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2005: Intelligent Data Engineering and Automated Learning - IDEAL 2005 pp 311–318Cite as

Universal Clustering with Family of Power Loss Functions in Probabilistic Space

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3578))

Abstract

We propose universal clustering in line with the concepts of universal estimation. In order to illustrate the model of universal clustering we consider family of power loss functions in probabilistic space which is marginally linked to the Kullback-Leibler divergence. The model proved to be effective in application to the synthetic data. Also, we consider large web-traffic dataset. The aim of the experiment is to explain and understand the way people interact with web sites.

This work was supported by the grants of the Australian Research Council

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Author information

Authors and Affiliations

  1. Computer Science Laboratory, Australian National University, Canberra, ACT 0200, Australia

    Vladimir Nikulin

Authors
  1. Vladimir Nikulin
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Editor information

Editors and Affiliations

  1. School of Information Technology and Electrical Engineering, University of Queensland, 4072, Australia

    Marcus Gallagher

  2.  , POB 30031, FL 32503-1031, Pensacola

    James P. Hogan

  3. Faculty of Information Technology, Queensland University of Technology, Box 2434, Q 4001, Brisbane, Australia

    Frederic Maire

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© 2005 Springer-Verlag Berlin Heidelberg

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Nikulin, V. (2005). Universal Clustering with Family of Power Loss Functions in Probabilistic Space. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_41

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  • DOI: https://doi.org/10.1007/11508069_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26972-4

  • Online ISBN: 978-3-540-31693-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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