השוואת שיטות

	מודל תערובת גאוסיאנית מרוגולרת	אשכול K-means
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2000s–2010s	1967 (formalized 1982)
הוגה השיטה	Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter)	MacQueen, J. B.; Lloyd, S. P.
סוג	Probabilistic clustering with regularization	Partitional clustering
מקור מכונן	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.
כינויים	Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
קשורות	5	4
תקציר	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.