Comparer des méthodes

	K-means en ligne	Regroupement hiérarchique
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1967 (online update rule); 2010 (mini-batch variant)	1963
Auteur d'origine	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)	Ward, J. H.
Type	Unsupervised clustering (online/streaming)	Unsupervised clustering (agglomerative)
Source fondatrice	MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297. University of California Press.	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.
Alias	sequential k-means, streaming k-means, incremental k-means, online clustering	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering
Apparentées	4	4
Résumé	Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would be too slow or impractical.	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.