Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Modelo de Mistura Gaussiana Bayesiana× | Agrupamento K-means× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1999–2006 | 1967 (formalized 1982) |
| Autor original≠ | Attias, H.; Bishop, C. M. | MacQueen, J. B.; Lloyd, S. P. |
| Tipo≠ | Probabilistic clustering / density estimation | Partitional clustering |
| Fonte seminal≠ | 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. DOI ↗ |
| Outros nomes | Bayesian GMM, Variational Gaussian Mixture, VBGMM, Dirichlet Process Gaussian Mixture | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Relacionados | 4 | 4 |
| Resumo≠ | 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. |
| ScholarGateConjunto de dados ↗ |
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