Sammenlign metoder

	BIRCH	DBSCAN
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1996	1996
Ophavsperson	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
Oprindelig kilde	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.
Aliasser	BIRCH clustering, CF-tree clustering, Balanced Iterative Reducing and Clustering using Hierarchies, incremental hierarchical clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Relaterede	2	3
Resumé	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.