Universal clustering with family of power loss functions in probabilistic space

Vladimir Nikulin

doi:10.1007/11508069_41

Universal clustering with family of power loss functions in probabilistic space

Vladimir Nikulin^*

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

4 Citations (Scopus)

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.

Original language	English
Pages (from-to)	311-318
Number of pages	8
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3578
DOIs	https://doi.org/10.1007/11508069_41
Publication status	Published - 2005
Event	6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005 - Brisbane, Australia Duration: 6 Jul 2005 → 8 Jul 2005

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10.1007/11508069_41

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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.",

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AB - 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.

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DO - 10.1007/11508069_41

M3 - Conference article

AN - SCOPUS:26444554889

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JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

T2 - 6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005

Y2 - 6 July 2005 through 8 July 2005

ER -

Universal clustering with family of power loss functions in probabilistic space

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