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Universal clustering with family of power loss functions in probabilistic space

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Date

2005

Authors

Nikulin, Vladimir

Journal Title

Journal ISSN

Volume Title

Publisher

Springer

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.

Description

Keywords

Keywords: Data structures; Mathematical models; Telecommunication traffic; Kullback-Leibler divergence; Power loss functions; Prbabilistic space; Probability

Citation

URI

http://hdl.handle.net/1885/52651

Collections

ANU Research Publications

Source

Type

Book chapter

Book Title

Intelligent Data Engineering and Automated Learning: proceedings of the 6th International Conference, IDEAL 2005, Brisbane, 6-8 July

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Access Statement

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