Comparer des méthodes

	BIRCH	DBSCAN
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1996	1996
Auteur d'origine	Zhang, T.; Ramakrishnan, R.; Livny, M.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Type	Incremental hierarchical clustering (CF-tree)	Density-based clustering algorithm
Source fondatrice	Zhang, T., Ramakrishnan, R., & Livny, M. (1996). BIRCH: An efficient data clustering method for very large databases. Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, 25(2), 103–114.	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	BIRCH clustering, CF-tree clustering, Balanced Iterative Reducing and Clustering using Hierarchies, incremental hierarchical clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Apparentées	2	3
Résumé	BIRCH is a scalable, incremental clustering algorithm introduced by Zhang, Ramakrishnan, and Livny in 1996. It is designed to cluster very large datasets — potentially larger than available memory — in a single pass, by compressing the data into a compact in-memory summary structure called a CF-tree (Clustering Feature tree) before applying any standard clustering procedure.	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.