Universal clustering with regularization in probabilistic space

Authors:
Vladimir Nikulin;Alex J. Smola
Affiliations:
Computer Science Laboratory, Australian National University, Canberra, Australia;NICTA, Canberra, Australia
Venue:
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
Year:
2005

Citing 3
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Abstract

We propose universal clustering in line with the concepts of universal estimation. In order to illustrate above model we introduce family of power loss functions in probabilistic space which is marginally linked to the Kullback-Leibler divergence. Above 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. The paper proposes special regularization in order to ensure consistency of the corresponding clustering model.