Comparison among Methods for k Estimation in k-means

Murilo C. Naldi, André Fontana, Ricardo J.G.B. Campello

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

Abstract

One of the most influential algorithms in data mining, k-means, is broadly used in practical tasks for its simplicity, computational efficiency and effectiveness in high dimensional problems. However, k-means has two major drawbacks, which are the need to choose the number of clusters, k, and the sensibility to the initial prototypes' position. In this work, systematic, evolutionary and order heuristics used to suppress these drawbacks are compared. 27 variants of 4 algorithmic approaches are used to partition 324 synthetic data sets and the obtained results are compared.

OriginalsprogEngelsk
TitelISDA 2009 - 9th International Conference on Intelligent Systems Design and Applications
ForlagIEEE
Publikationsdato2009
Sider1006-1013
Artikelnummer5364434
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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