Comparar métodos

	K-means Online	Mapa Auto-Organizável (Mapa de Kohonen)
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1967 (online update rule); 2010 (mini-batch variant)	1982
Autor original	MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant)	Teuvo Kohonen
Tipo	Unsupervised clustering (online/streaming)	Unsupervised neural network for topology-preserving mapping
Fonte seminal	MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297. University of California Press.	Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.
Outros nomes	sequential k-means, streaming k-means, incremental k-means, online clustering	SOM, Kohonen map, Kohonen network, öz-örgütlemeli harita
Relacionados	4	3
Resumo	Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would be too slow or impractical.	A self-organizing map is an unsupervised neural network, introduced by Teuvo Kohonen in 1982, that projects high-dimensional data onto a low-dimensional (usually two-dimensional) grid of prototype vectors while preserving the data's topology — nearby inputs map to nearby grid cells. It is used for visualization, clustering, and exploratory analysis, turning complex data into an ordered, interpretable map.