Comparar métodos

	Mapa Auto-Organizável (Mapa de Kohonen)	Agrupamento K-Means
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1982	1967
Autor original	Teuvo Kohonen	MacQueen, J.
Tipo	Unsupervised neural network for topology-preserving mapping	Partitional clustering (centroid-based)
Fonte seminal	Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
Outros nomes	SOM, Kohonen map, Kohonen network, öz-örgütlemeli harita	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Relacionados	3	3
Resumo	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.	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.