방법 비교

	Robust Gaussian Mixture Model	로버스트 k-평균
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2000	1999
창시자	Peel, D. & McLachlan, G. J.	Garcia-Escudero, L. A. & Gordaliza, A.
유형	Probabilistic clustering / density estimation	Robust clustering algorithm
원전	Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10(4), 339–348.	Garcia-Escudero, L. A., & Gordaliza, A. (1999). Robustness properties of k-means and trimmed k-means. Journal of the American Statistical Association, 94(447), 956–969.
별칭	Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model	robust k-means clustering, trimmed k-means, outlier-resistant k-means, RKM
관련	5	4
요약	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.	Robust k-means is a variant of classical k-means clustering designed to resist the influence of outliers. By trimming a specified fraction of the most extreme observations before computing cluster centers, it produces stable and meaningful partitions even when the data contain noise, contamination, or heavy-tailed distributions — situations where standard k-means breaks down.