পদ্ধতির তুলনা করুন

	বেয়েশীয় গাউসীয় মিশ্রণ মডেল (Bayesian Gaussian Mixture Model)	কে-মিনস ক্লাস্টারিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1999–2006	1967 (formalized 1982)
প্রবর্তক	Attias, H.; Bishop, C. M.	MacQueen, J. B.; Lloyd, S. P.
ধরন	Probabilistic clustering / density estimation	Partitional clustering
মৌলিক উৎস	Bishop, C. M. (2006). Pattern Recognition and Machine Learning (Ch. 10). Springer. ISBN: 978-0-387-31073-2	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
অপর নাম	Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
সম্পর্কিত	4	4
সারসংক্ষেপ	The Bayesian Gaussian Mixture Model places prior distributions over all mixture parameters and infers their posteriors — typically via Variational Bayes or MCMC — rather than fitting fixed point estimates. This yields principled uncertainty quantification, automatic selection of the effective number of components, and resistance to overfitting small datasets.	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.