Fast evolutionary algorithms for relational clustering clustering

Danilo Horta*, Ricardo J.G.B. Campello

*Kontaktforfatter

Publikation: Kapitel i bog/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Abstract

This paper is concerned with the computational efficiency of clustering algorithms when the data set to be clustered is described by a proximity matrix only (relational data) and the number of clusters must be automatically estimated from such data. Two relational versions of an evolutionary algorithm for clustering are derived and compared against two systematic (repetitive) approaches that can also be used to automatically estimate the number of clusters in relational data. Exhaustive experiments involving six artificial and two real data sets are reported and analyzed.

OriginalsprogEngelsk
TitelISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications
ForlagIEEE
Publikationsdato2009
Sider1456-1462
Artikelnummer5363381
ISBN (Trykt)9780769538723
DOI
StatusUdgivet - 2009
Udgivet eksterntJa
Begivenhed9th International Conference on Intelligent Systems Design and Applications, ISDA 2009 - Pisa, Italien
Varighed: 30. nov. 20092. dec. 2009

Konference

Konference9th International Conference on Intelligent Systems Design and Applications, ISDA 2009
Land/OmrådeItalien
ByPisa
Periode30/11/200902/12/2009
SponsorInternational Fuzzy Systems Association, MIR Labs, Universidad de Granada, UGR, Universita de Pisa, University of Salerno
NavnInternational Conference on Intelligent Systems Design and Applications, ISDA
ISSN2164-7143

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