Comparer des méthodes

	K-means en ligne	DBSCAN
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1967 (online update rule); 2010 (mini-batch variant)	1996
Auteur d'origine	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Type	Unsupervised clustering (online/streaming)	Density-based clustering algorithm
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.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.
Alias	sequential k-means, streaming k-means, incremental k-means, online clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Apparentées	4	3
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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.