Comparar métodos

	Clustering K-means Bayesiano	Análisis de clases latentes (LCA)
Campo	Estadística	Estadística
Familia	Latent structure	Latent structure
Año de origen	2006–2012	1950s–1968
Autor original	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	Paul F. Lazarsfeld
Tipo	Probabilistic clustering / Bayesian nonparametric	Latent variable / person-centered classification
Fuente seminal	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.	Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61(2), 215–231.
Alias	Bayesian K-means, probabilistic K-means, Dirichlet K-means, BKM	LCA, latent class model, latent categorical analysis, finite mixture of multinomials
Relacionados	6	6
Resumen	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.	Latent class analysis identifies unobserved subgroups — latent classes — within a population by finding patterns of responses across a set of categorical observed indicators. It is the categorical-variable counterpart of cluster analysis, but grounded in an explicit probabilistic model, and is widely used in social, health, and behavioral sciences to discover typologies in survey or diagnostic data.