Comparer des méthodes

	Regroupement hiérarchique robuste	Modélisation par mélange
Domaine	Statistique	Statistique
Famille	Latent structure	Latent structure
Année d'origine	1990	1894
Auteur d'origine	Kaufman & Rousseeuw (building on Ward, 1963 and others)	Karl Pearson
Type	Robust unsupervised clustering	Latent variable / density estimation
Source fondatrice	Kaufman, L. & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. ISBN: 978-0471878766	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
Alias	robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC	finite mixture model, mixture distribution model, FMM, model-based clustering
Apparentées	5	6
Résumé	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.	Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.