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Universal clustering with family of power loss functions in probabilistic space

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dc.contributor.authorNikulin, Vladimir
dc.date.accessioned2015-12-10T22:23:08Z
dc.date.available2015-12-10T22:23:08Z
dc.date.issued2005
dc.date.updated2015-12-09T09:05:01Z
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.
dc.identifier.isbn354026972X
dc.identifier.urihttp://hdl.handle.net/1885/52651
dc.publisherSpringer
dc.relation.ispartofIntelligent Data Engineering and Automated Learning: proceedings of the 6th International Conference, IDEAL 2005, Brisbane, 6-8 July
dc.relation.isversionof1 Edition
dc.subjectKeywords: Data structures; Mathematical models; Telecommunication traffic; Kullback-Leibler divergence; Power loss functions; Prbabilistic space; Probability
dc.titleUniversal clustering with family of power loss functions in probabilistic space
dc.typeBook chapter
local.bibliographicCitation.lastpage318
local.bibliographicCitation.placeofpublicationBerlin Germany
local.bibliographicCitation.startpage311
local.contributor.affiliationNikulin, Vladimir, College of Engineering and Computer Science, ANU
local.contributor.authoruidNikulin, Vladimir, u4119285
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080109 - Pattern Recognition and Data Mining
local.identifier.ariespublicationMigratedxPub252
local.identifier.scopusID2-s2.0-26444554889
local.type.statusPublished Version

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