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http://hdl.handle.net/10773/32548
Title: | CDPCA: 10 years after |
Author: | Freitas, Adelaide |
Keywords: | Clustering Sparse principal components analysis |
Issue Date: | Feb-2021 |
Publisher: | Sociedade Portuguesa de Estatística |
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. |
Peer review: | yes |
URI: | http://hdl.handle.net/10773/32548 |
ISBN: | 978-972-8890-47-6 |
Publisher Version: | https://www.spestatistica.pt/pt/publicacoes/publicacao/estatistica-desafios-transversais-ciencias-com-dados |
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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