Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/32548
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Freitas, Adelaide | pt_PT |
dc.date.accessioned | 2021-11-04T12:07:38Z | - |
dc.date.available | 2021-11-04T12:07:38Z | - |
dc.date.issued | 2021-02 | - |
dc.identifier.isbn | 978-972-8890-47-6 | pt_PT |
dc.identifier.uri | http://hdl.handle.net/10773/32548 | - |
dc.description.abstract | Clustering and Disjoint Principal Component Analysis (CDPCA) is a constrained principal component analysis for multivariate numerical data. The main goal is to detect clusters of objects and, simultaneously, to fi nd a partitioning of variables such that the between cluster deviance in the reduced space of such partition is maximized. The partition formed by a disjoint set of the original variables identifi es the groups of variables belonging to the CDPCA components. Recently, this methodology has been implemented in a R-function called CDpca. In this work, we review some theoretical issues of the CDPCA model and present two applications on real data sets using the R-function CDpca. | pt_PT |
dc.language.iso | eng | pt_PT |
dc.publisher | Sociedade Portuguesa de Estatística | pt_PT |
dc.relation | UIDB/04106/2020 | pt_PT |
dc.rights | openAccess | pt_PT |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | pt_PT |
dc.subject | Clustering | pt_PT |
dc.subject | Sparse principal components analysis | pt_PT |
dc.title | CDPCA: 10 years after | pt_PT |
dc.type | bookPart | pt_PT |
dc.description.version | published | pt_PT |
dc.peerreviewed | yes | pt_PT |
degois.publication.firstPage | 1 | pt_PT |
degois.publication.lastPage | 11 | pt_PT |
degois.publication.location | Lisboa | pt_PT |
degois.publication.title | Estatística: desafios transversais às ciências com dados: atas do XXIV Congresso da Sociedade Portuguesa de Estatística | pt_PT |
dc.relation.publisherversion | https://www.spestatistica.pt/pt/publicacoes/publicacao/estatistica-desafios-transversais-ciencias-com-dados | pt_PT |
Appears in Collections: | CIDMA - Capítulo de livro DMat - Capítulo de livro PSG - Capítulo de livro |
Files in This Item:
File | Description | Size | Format | |
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2021Freitas2021_CDPCA_10 years after.pdf | 386.9 kB | Adobe PDF | View/Open |
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