Identification of noisy variables for nonmetric and symbolic data in cluster analysis

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dc.contributor.authorWalesiak, Marek
dc.contributor.authorDudek, Andrzej
dc.contributor.organizationUniwersytet Ekonomiczny we Wrocławiuen
dc.date.accessioned2013-03-09T22:20:25Z
dc.date.available2013-03-09T22:20:25Z
dc.date.issued2008
dc.description.abstractA proposal of an extended version of the HINoV method for the iden- tification of the noisy variables (Carmone et al [1999]) for nonmetric, mixed, and symbolic interval data is presented in this paper. Proposed modifications are eval- uated on simulated data from a variety of models. The models contain the known structure of clusters. In addition, the models contain a different number of noisy (irrelevant) variables added to obscure the underlying structure to be recovered.en
dc.description.epersonMarek Walesiak
dc.identifier.isbn978-3-540-78239-1
dc.identifier.issn1431-8814
dc.identifier.urihttps://open.icm.edu.pl/handle/123456789/1049
dc.language.isoenen
dc.publisherSpringer-Verlagen
dc.rightsDozwolony użytek
dc.subjectclusterSimen
dc.subjectnonmetric and symbolic dataen
dc.subjectHINoV methoden
dc.subjectvariable selectionen
dc.titleIdentification of noisy variables for nonmetric and symbolic data in cluster analysisen
dc.typearticleen
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