Comparer des méthodes

	Partitionnement K-means Robuste	Regroupement hiérarchique robuste
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1997	1990
Auteur d'origine	Cuesta-Albertos, Gordaliza & Matrán	Kaufman & Rousseeuw (building on Ward, 1963 and others)
Type	Robust partitional clustering	Robust unsupervised clustering
Source fondatrice	Cuesta-Albertos, J. A., Gordaliza, A., & Matrán, C. (1997). Trimmed k-means: An attempt to robustify quantizers. The Annals of Statistics, 25(2), 553–576.	Kaufman, L. & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. ISBN: 978-0471878766
Alias	trimmed k-means, TCLUST k-means, contamination-resistant k-means, outlier-robust clustering	robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC
Apparentées	4	5
Résumé	Robust K-means clustering is an extension of classical k-means that protects cluster estimates from distortion caused by outliers or contaminated observations. By trimming a user-specified fraction of the most extreme points before updating cluster centers, the algorithm yields stable, meaningful partitions even when the data contain atypical cases that would severely bias standard k-means.	Robust hierarchical clustering extends classical agglomerative or divisive hierarchical clustering by replacing sensitive distance measures and linkage criteria with outlier-resistant alternatives, preserving cluster structure even when data contain anomalous observations or heavy-tailed distributions.