Compară metode

	DBSCAN	K-means online
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1996	1967 (online update rule); 2010 (mini-batch variant)
Autorul original	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)
Tip	Density-based clustering algorithm	Unsupervised clustering (online/streaming)
Sursa seminală	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.	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.
Denumiri alternative	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	sequential k-means, streaming k-means, incremental k-means, online clustering
Înrudite	3	4
Rezumat	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.	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.