Comparar métodos

	Clustering jerárquico robusto	Escalamiento multidimensional (MDS)
Campo	Estadística	Estadística
Familia	Latent structure	Latent structure
Año de origen	1990	1952–1964
Autor original	Kaufman & Rousseeuw (building on Ward, 1963 and others)	Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)
Tipo	Robust unsupervised clustering	Dimensionality reduction / visualization
Fuente seminal	Kaufman, L. & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. ISBN: 978-0471878766	Kruskal, J. B. (1964). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1–27.
Alias	robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC	MDS, metric MDS, non-metric MDS, proximity scaling
Relacionados	5	5
Resumen	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.	Multidimensional scaling maps objects described only by pairwise similarities or dissimilarities into a low-dimensional geometric space so that distances in that space reflect the original proximity structure as faithfully as possible. It is widely used to visualize the hidden structure of psychological, social, and behavioral data.