Salīdzināt metodes

	DBSCAN	Tiešsaistes Gausa maisījuma modelis
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1996	2000–2009
Autors	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Cappé, O. & Moulines, E. (online EM formulation)
Tips	Density-based clustering algorithm	Probabilistic clustering / density estimation (incremental)
Pirmavots	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.	Cappé, O. & Moulines, E. (2009). On-line expectation-maximization algorithm for latent data models. Journal of the Royal Statistical Society: Series B, 71(3), 593–613.
Citi nosaukumi	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Online GMM, Incremental GMM, Streaming Gaussian Mixture Model, Sequential GMM
Saistītās	3	5
Kopsavilkums	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.	Online Gaussian Mixture Model adapts the classic GMM to streaming or large-scale data by replacing full-batch EM with incremental updates — processing one observation or mini-batch at a time and continuously refining component means, covariances, and mixing weights without revisiting the entire dataset.