Methoden vergleichen

	K-means Clustering	Regularized Gaussian Mixture Model
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	1967 (formalized 1982)	2000s–2010s
Urheber	MacQueen, J. B.; Lloyd, S. P.	Fraley, C. & Raftery, A. E. (regularization formalized); sklearn team (practical reg_covar parameter)
Typ	Partitional clustering	Probabilistic clustering with regularization
Wegweisende Quelle	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Fraley, C. & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611–631.
Aliasnamen	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Regularized GMM, GMM with covariance regularization, stabilized Gaussian mixture model, penalized GMM
Verwandt	4	5
Zusammenfassung	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.	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.