Automatic aspect discrimination in relational data clustering

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

*Kontaktforfatter

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

Abstract

The features describing a data set may often be arranged in meaningful subsets, each of which corresponds to a different aspect of the data. An unsupervised algorithm (SCAD) that performs fuzzy clustering and aspects weighting simultaneously was recently proposed. However, there are several situations where the data set is represented by proximity matrices only (relational data), which renders several clustering approaches, including SCAD, inappropriate. To handle this kind of data, the relational clustering algorithm CARD, based on the SCAD algorithm, has been recently developed. However, CARD may fail and halt given certain conditions. To fix this problem, its steps are modified and then reordered to also reduce the number of parameters required. The improved CARD is assessed over hundreds of real and artificial data sets.

OriginalsprogEngelsk
TitelProceedings of the 2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11
ForlagIEEE
Publikationsdato2011
Sider522-529
Artikelnummer6121709
ISBN (Trykt)9781457716751
DOI
StatusUdgivet - 2011
Udgivet eksterntJa
Begivenhed2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11 - Cordoba, Spanien
Varighed: 22. nov. 201124. nov. 2011

Konference

Konference2011 11th International Conference on Intelligent Systems Design and Applications, ISDA'11
Land/OmrådeSpanien
ByCordoba
Periode22/11/201124/11/2011
SponsorMachine Intelligence Research Labs (MIR Labs), University of Córdoba, Ministry of Science and Innovation of Spain
NavnInternational Conference on Intelligent Systems Design and Applications, ISDA
ISSN2164-7143

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