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Universal clustering with family of power loss functions in probabilistic space

Nikulin, Vladimir


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.

CollectionsANU Research Publications
Date published: 2005
Type: Book chapter


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