A robust methodology for comparing performances of clustering validity criteria

Lucas Vendramin*, Ricardo J.G.B. Campello, Eduardo R. Hruschka

*Kontaktforfatter

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

Abstract

Many different clustering validity measures exist that are very useful in practice as quantitative criteria for evaluating the quality of data partitions. However, it is a hard task for the user to choose a specific measure when he or she faces such a variety of possibilities. The present paper introduces an alternative, robust methodology for comparing clustering validity measures that has been especially designed to get around some conceptual flaws of the comparison paradigm traditionally adopted in the literature. An illustrative example involving the comparison of the performances of four well-known validity measures over a collection of 7776 data partitions of 324 different data sets is presented.

OriginalsprogEngelsk
TitelAdvances in Artificial Intelligence - SBIA 2008 - 19th Brazilian Symposium on Artificial Intelligence, Proceedings
ForlagSpringer
Publikationsdato2008
Sider237-247
ISBN (Trykt)3540881891, 9783540881896
DOI
StatusUdgivet - 2008
Udgivet eksterntJa
Begivenhed19th Brazilian Symposium on Artificial Intelligence, SBIA 2008 - Salvador, Brasilien
Varighed: 26. okt. 200830. okt. 2008

Konference

Konference19th Brazilian Symposium on Artificial Intelligence, SBIA 2008
Land/OmrådeBrasilien
BySalvador
Periode26/10/200830/10/2008
SponsorConselho Nac. de Desenvolvimento Cient. e Tecnol. (CNPq), Coord. de Aperfeicoamento de Pessoal de Nivel Super. (CAPES)
NavnLecture Notes in Computer Science
Vol/bind5249 LNAI
ISSN0302-9743

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