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Universal clustering with regularization in probabilistic space

Nikulin, Vladimir; Smola, Alexander


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...[Show more]

CollectionsANU Research Publications
Date published: 2005
Type: Book chapter
Book Title: Machine Learning and Data Mining in Pattern Recognition


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