So sánh phương pháp

	Mô hình Hỗn hợp Gaussian Mạnh mẽ	Phân cụm K-means
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2000	1967 (formalized 1982)
Người khởi xướng	Peel, D. & McLachlan, G. J.	MacQueen, J. B.; Lloyd, S. P.
Loại	Probabilistic clustering / density estimation	Partitional clustering
Công trình gốc	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.
Tên gọi khác	Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Liên quan	5	4
Tóm tắt	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.