Comparer des méthodes

	Analyse de regroupement robuste (TCLUST)	Analyse en Composantes Principales Robuste (RPCA)
Domaine	Statistique	Statistique
Famille	Regression model	Regression model
Année d'origine	2008	2011
Auteur d'origine	García-Escudero, Gordaliza, Matrán & Mayo-Iscar (TCLUST)	Candès, Li, Ma & Wright (2011); Hubert, Rousseeuw & Vanden Branden (2005)
Type	Robust model-based clustering	Robust dimensionality reduction / matrix decomposition
Source fondatrice	García-Escudero, L. A., Gordaliza, A., Matrán, C., & Mayo-Iscar, A. (2008). A General Trimming Approach to Robust Cluster Analysis. The Annals of Statistics, 36(3), 1324-1345.	Candès, E. J., Li, X., Ma, Y., & Wright, J. (2011). Robust Principal Component Analysis? Journal of the ACM, 58(3), 1-37.
Alias	TCLUST, trimmed clustering, robust clustering, Robust Küme Analizi (TCLUST)	RPCA, robust principal component analysis, low-rank plus sparse decomposition, Robust Temel Bileşen Analizi (RPCA)
Apparentées	5	3
Résumé	Robust Cluster Analysis is a trimmed model-based clustering method, introduced by García-Escudero and colleagues in 2008, that partitions continuous multivariate data into clusters while resisting the influence of outliers and noise. By setting aside a fraction of the most discordant observations, it keeps the recovered cluster structure from being contaminated by stray points.	Robust Principal Component Analysis is a dimensionality-reduction method that extracts reliable components when the data are contaminated by outliers and noise. Introduced by Candès, Li, Ma and Wright (2011), and developed in the ROBPCA approach of Hubert, Rousseeuw and Vanden Branden (2005), it separates a data matrix into a clean low-rank part and a sparse outlier part.