Universal clustering with family of power loss functions in probabilistic space

dc.contributor.authorNikulin, Vladimiren
dc.date.accessioned2025-12-31T21:42:16Z
dc.date.available2025-12-31T21:42:16Z
dc.date.issued2005en
dc.description.abstractWe 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.en
dc.description.statusPeer-revieweden
dc.format.extent8en
dc.identifier.issn0302-9743en
dc.identifier.scopus26444554889en
dc.identifier.urihttps://hdl.handle.net/1885/733798364
dc.language.isoenen
dc.relation.ispartofseries6th International Conference on Intelligent Data Engineering and Automated Learning - IDEAL 2005en
dc.sourceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en
dc.titleUniversal clustering with family of power loss functions in probabilistic spaceen
dc.typeConference paperen
dspace.entity.typePublicationen
local.bibliographicCitation.lastpage318en
local.bibliographicCitation.startpage311en
local.contributor.affiliationNikulin, Vladimir; School of Computing, ANU College of Systems and Society, The Australian National Universityen
local.identifier.ariespublicationMigratedxPub252en
local.identifier.citationvolume3578en
local.identifier.doi10.1007/11508069_41en
local.identifier.puref3b7af6d-e5c3-4b77-9d5b-5ca055a23ec9en
local.identifier.urlhttps://www.scopus.com/pages/publications/26444554889en
local.type.statusPublisheden

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