手法を比較

	ロバスト階層的クラスタリング	多次元尺度構成法 (MDS)
分野	統計学	統計学
系統	Latent structure	Latent structure
提唱年	1990	1952–1964
提唱者	Kaufman & Rousseeuw (building on Ward, 1963 and others)	Warren S. Torgerson (metric MDS, 1952); Joseph B. Kruskal (non-metric MDS, 1964)
種類	Robust unsupervised clustering	Dimensionality reduction / visualization
原典	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.
別名	robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC	MDS, metric MDS, non-metric MDS, proximity scaling
関連	5	5
概要	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.