Сравнение на методи

	Robust Hierarchical Clustering	Многомерно скалиране (MDS)
Област	Статистика	Статистика
Семейство	Latent structure	Latent structure
Година на възникване	1990	1952–1964
Създател	Kaufman & Rousseeuw (building on Ward, 1963 and others)	Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)
Тип	Robust unsupervised clustering	Dimensionality reduction / visualization
Основополагащ източник	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.
Други названия	robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC	MDS, metric MDS, non-metric MDS, proximity scaling
Свързани	5	5
Резюме	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.