Сравнение на методи

	Robust Hierarchical Clustering	Йерархично групиране
Област	Статистика	Машинно обучение
Семейство	Latent structure	Machine learning
Година на възникване	1990	1963
Създател	Kaufman & Rousseeuw (building on Ward, 1963 and others)	Ward, J. H.
Тип	Robust unsupervised clustering	Unsupervised clustering (agglomerative)
Основополагащ източник	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.
Други названия	robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Свързани	5	4
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.	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.