পদ্ধতির তুলনা করুন

	দৃঢ় গাউসিয়ান মিশ্র মডেল	কে-মিনস ক্লাস্টারিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2000	1967 (formalized 1982)
প্রবর্তক	Peel, D. & McLachlan, G. J.	MacQueen, J. B.; Lloyd, S. P.
ধরন	Probabilistic clustering / density estimation	Partitional clustering
মৌলিক উৎস	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.
অপর নাম	Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
সম্পর্কিত	5	4
সারসংক্ষেপ	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.