Confronta i metodi

	Clustering gerarchico robusto	Clustering gerarchico
Campo	Statistica	Apprendimento automatico
Famiglia	Latent structure	Machine learning
Anno di origine	1990	1963
Ideatore	Kaufman & Rousseeuw (building on Ward, 1963 and others)	Ward, J. H.
Tipo	Robust unsupervised clustering	Unsupervised clustering (agglomerative)
Fonte seminale	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.
Alias	robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Correlati	5	4
Sintesi	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.