Confronta i metodi

	Modello di Miscela Gaussiana	DBSCAN
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	1977	1996
Ideatore	Dempster, Laird & Rubin (EM algorithm)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Tipo	Probabilistic (soft) clustering — mixture model	Density-based clustering algorithm
Fonte seminale	Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22.	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	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Correlati	4	3
Sintesi	A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation.	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.