Compară metode

	Clustering K-means robust	Clustering ierarhic robust
Domeniu	Statistică	Statistică
Familie	Latent structure	Latent structure
Anul apariției	1997	1990
Autorul original	Cuesta-Albertos, Gordaliza & Matrán	Kaufman & Rousseeuw (building on Ward, 1963 and others)
Tip	Robust partitional clustering	Robust unsupervised clustering
Sursa seminală	Cuesta-Albertos, J. A., Gordaliza, A., & Matrán, C. (1997). Trimmed k-means: An attempt to robustify quantizers. The Annals of Statistics, 25(2), 553–576.	Kaufman, L. & Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley. ISBN: 978-0471878766
Denumiri alternative	trimmed k-means, TCLUST k-means, contamination-resistant k-means, outlier-robust clustering	robust agglomerative clustering, outlier-resistant hierarchical clustering, robust linkage clustering, RHC
Înrudite	4	5
Rezumat	Robust K-means clustering is an extension of classical k-means that protects cluster estimates from distortion caused by outliers or contaminated observations. By trimming a user-specified fraction of the most extreme points before updating cluster centers, the algorithm yields stable, meaningful partitions even when the data contain atypical cases that would severely bias standard k-means.	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.