방법 비교

	가우시안 혼합 모형	DBSCAN
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1977	1996
창시자	Dempster, Laird & Rubin (EM algorithm)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
유형	Probabilistic (soft) clustering — mixture model	Density-based clustering algorithm
원전	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.
별칭	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
관련	4	3
요약	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.