Compara mètodes

	DBSCAN en línia	DBSCAN	K-means en línia
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	1998	1996	1967 (online update rule); 2010 (mini-batch variant)
Autor original	Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)
Tipus	Incremental density-based clustering	Density-based clustering algorithm	Unsupervised clustering (online/streaming)
Font seminal	Ester, M., Kriegel, H.-P., Sander, J., Wimmer, M., & Xu, X. (1998). Incremental Clustering for Mining in a Data Warehousing Environment. In Proceedings of the 24th International Conference on Very Large Data Bases (VLDB), pp. 323–333.	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.
Àlies	Incremental DBSCAN, Streaming DBSCAN, Online density-based clustering, iDBSCAN	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	sequential k-means, streaming k-means, incremental k-means, online clustering
Relacionats	5	3	4
Resum	Online DBSCAN extends the classic density-based clustering algorithm to handle continuously arriving data points without re-clustering the entire dataset from scratch. Each new observation is integrated into the existing cluster structure by local neighborhood queries, making it practical for streaming and data-warehousing scenarios where data grows incrementally.	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.