Confronta i metodi

	BIRCH	Clustering K-means
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	1996	1967 (formalized 1982)
Ideatore	Zhang, T.; Ramakrishnan, R.; Livny, M.	MacQueen, J. B.; Lloyd, S. P.
Tipo	Incremental hierarchical clustering (CF-tree)	Partitional clustering
Fonte seminale	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Alias	BIRCH clustering, CF-tree clustering, Balanced Iterative Reducing and Clustering using Hierarchies, incremental hierarchical clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Correlati	2	4
Sintesi	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.	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.