পদ্ধতির তুলনা করুন

	ডিবিস্ক্যান	ফ্যাক্টর অ্যানালাইসিস (Factor Analysis)	গাউসিয়ান মিক্সচার মডেল
ক্ষেত্র	যন্ত্র শিখন	গবেষণা পরিসংখ্যান	যন্ত্র শিখন
পরিবার	Machine learning	Process / pipeline	Machine learning
উদ্ভবের বছর	1996	1931	1977
প্রবর্তক	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Louis Leon Thurstone	Dempster, Laird & Rubin (EM algorithm)
ধরন	Density-based clustering algorithm	Method	Probabilistic (soft) clustering — mixture model
মৌলিক উৎস	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.
অপর নাম	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
সম্পর্কিত	3	3	4
সারসংক্ষেপ	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.