Krahasoni metodat

	Grupimi Hierarkik	DBSCAN	Modeli i përzierjes Gaussian
Fusha	Mësimi i makinës	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning	Machine learning
Viti i origjinës	1963	1996	1977
Krijuesi	Ward, J. H.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Dempster, Laird & Rubin (EM algorithm)
Lloji	Unsupervised clustering (agglomerative)	Density-based clustering algorithm	Probabilistic (soft) clustering — mixture model
Burimi themelues	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	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.	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.
Emërtime të tjera	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians
Të lidhura	4	3	4
Përmbledhja	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.	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.	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.