Comparer des méthodes

	k-means robuste	Regroupement par K-moyennes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1999	1967 (formalized 1982)
Auteur d'origine	Garcia-Escudero, L. A. & Gordaliza, A.	MacQueen, J. B.; Lloyd, S. P.
Type	Robust clustering algorithm	Partitional clustering
Source fondatrice	Garcia-Escudero, L. A., & Gordaliza, A. (1999). Robustness properties of k-means and trimmed k-means. Journal of the American Statistical Association, 94(447), 956–969.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Alias	robust k-means clustering, trimmed k-means, outlier-resistant k-means, RKM	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Apparentées	4	4
Résumé	Robust k-means is a variant of classical k-means clustering designed to resist the influence of outliers. By trimming a specified fraction of the most extreme observations before computing cluster centers, it produces stable and meaningful partitions even when the data contain noise, contamination, or heavy-tailed distributions — situations where standard k-means breaks down.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.