Сравнение на методи

	Robust Gaussian Mixture Model	K-means клъстеризация
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2000	1967 (formalized 1982)
Създател	Peel, D. & McLachlan, G. J.	MacQueen, J. B.; Lloyd, S. P.
Тип	Probabilistic clustering / density estimation	Partitional clustering
Основополагащ източник	Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10(4), 339–348.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Други названия	Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Свързани	5	4
Резюме	Robust Gaussian Mixture Model replaces the standard Gaussian components with heavier-tailed distributions — most commonly Student's t-distributions — or incorporates trimming and down-weighting of outliers within the EM framework. The result is a probabilistic clustering and density-estimation method that assigns genuinely anomalous points less influence on component parameters, preventing outliers from distorting cluster shapes or positions.	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.