השוואת שיטות

	DBSCAN	ניתוח גורמים	מודל תערובת גאוסיאנית
תחום	למידת מכונה	סטטיסטיקה למחקר	למידת מכונה
משפחה	Machine learning	Process / pipeline	Machine learning
שנת המקור	1996	1931	1977
הוגה השיטה	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Louis Leon Thurstone	Dempster, Laird & Rubin (EM algorithm)
סוג	Density-based clustering algorithm	Method	Probabilistic (soft) clustering — mixture model
מקור מכונן	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.	Thurstone, L. L. (1947). Multiple Factor Analysis. University of Chicago Press.	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.
כינויים	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	EFA, CFA, latent variable modeling	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians
קשורות	3	3	4
תקציר	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.	Factor analysis is a statistical technique for identifying latent (unobserved) dimensions underlying observed variables, developed by Louis Leon Thurstone in the 1930s and formalized by Jöreskog (1969). Exploratory factor analysis (EFA) discovers unknown factor structure from data; confirmatory factor analysis (CFA) tests hypothesized relationships between observed and latent variables. Essential in psychometrics (test development), organizational research (measuring constructs like leadership style), and biomedicine (identifying disease subtypes), factor analysis reduces dimensionality while revealing conceptual organization in multivariate data.	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.