手法を比較

	ベイズ的K平均法クラスタリング	混合モデル (Mixture Modeling)
分野	統計学	統計学
系統	Latent structure	Latent structure
提唱年	2006–2012	1894
提唱者	Kulis & Jordan (ICML 2012) formalized the Bayesian nonparametric derivation; Bishop (2006) established the variational Bayesian EM framework for Gaussian mixture models as a probabilistic foundation	Karl Pearson
種類	Probabilistic clustering / Bayesian nonparametric	Latent variable / density estimation
原典	Kulis, B. & Jordan, M. I. (2012). Revisiting k-means: New algorithms via Bayesian nonparametrics. In Proceedings of the 29th International Conference on Machine Learning (ICML), Edinburgh, Scotland, pp. 513–520.	McLachlan, G. J. & Peel, D. (2000). Finite Mixture Models. Wiley-Interscience. ISBN: 978-0471006268
別名	Bayesian K-means, probabilistic K-means, Dirichlet K-means, BKM	finite mixture model, mixture distribution model, FMM, model-based clustering
関連	6	6
概要	Bayesian K-means clustering extends the classical K-means algorithm by placing prior distributions over cluster centroids and mixing proportions. This probabilistic framework provides uncertainty estimates for cluster assignments, allows principled model selection for the number of clusters, and regularises centroid estimation — especially valuable when data are scarce or high-dimensional.	Mixture modeling assumes that a population is composed of K unobserved subpopulations, each described by its own probability distribution. The observed data are treated as draws from a weighted combination of these component distributions. It provides a principled, model-based alternative to ad hoc clustering and supports formal comparison of solutions with different numbers of components.