CDPCA: 10 years after
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 s...
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| Formato: | bookPart |
| Idioma: | eng |
| Publicado em: |
2021
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10773/32548 |
| País: | Portugal |
| Oai: | oai:ria.ua.pt:10773/32548 |
| Resumo: | 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. |
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