Compara mètodes

	BIRCH	DBSCAN	Agrupació K-means
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	1996	1996	1967 (formalized 1982)
Autor original	Zhang, T.; Ramakrishnan, R.; Livny, M.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	MacQueen, J. B.; Lloyd, S. P.
Tipus	Incremental hierarchical clustering (CF-tree)	Density-based clustering algorithm	Partitional clustering
Font seminal	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Àlies	BIRCH clustering, CF-tree clustering, Balanced Iterative Reducing and Clustering using Hierarchies, incremental hierarchical clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Relacionats	2	3	4
Resum	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.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.