Comparar métodos

	Modelo de Mistura Gaussiana Regularizado	Agrupamento K-means
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2000s–2010s	1967 (formalized 1982)
Autor original	Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter)	MacQueen, J. B.; Lloyd, S. P.
Tipo	Probabilistic clustering with regularization	Partitional clustering
Fonte seminal	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Outros nomes	Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Relacionados	5	4
Resumo	A Regularized Gaussian Mixture Model (GMM) adds a small positive constant to the diagonal of each component covariance matrix during the Expectation-Maximization algorithm, preventing singular or near-singular matrices that cause numerical failures when the data are sparse, high-dimensional, or contain near-duplicate observations.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.