방법 비교

	강건 계층적 군집화	계층적 군집화
분야	통계학	머신러닝
계열	Latent structure	Machine learning
기원 연도	1990	1963
창시자	Kaufman & Rousseeuw (building on Ward, 1963 and others)	Ward, J. H.
유형	Robust unsupervised clustering	Unsupervised clustering (agglomerative)
원전	Kaufman, L. & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. ISBN: 978-0471878766	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
별칭	robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
관련	5	4
요약	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.	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.