Porovnať metódy

	Samostatne organizujúca sa mapa (mapa Kohonena)	K-Means Clustering
Odbor	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning
Rok vzniku	1982	1967
Tvorca	Teuvo Kohonen	MacQueen, J.
Typ	Unsupervised neural network for topology-preserving mapping	Partitional clustering (centroid-based)
Pôvodný zdroj	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.
Ďalšie názvy	SOM, Kohonen map, Kohonen network, öz-örgütlemeli harita	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Príbuzné	3	3
Zhrnutie	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.