Please use this identifier to cite or link to this item:
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
ISBN: 978-972-8890-47-6
Publisher Version:
Appears in Collections:CIDMA - Capítulo de livro
DMat - Capítulo de livro
PSG - Capítulo de livro

Files in This Item:
File Description SizeFormat 
2021Freitas2021_CDPCA_10 years after.pdf386.9 kBAdobe PDFView/Open

Formato BibTex MendeleyEndnote Degois 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.