Сравнение на методи

	Самоорганизираща се карта (Карта на Кохонен)	Клъстериране с К-средни
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	1982	1967
Създател	Teuvo Kohonen	MacQueen, J.
Тип	Unsupervised neural network for topology-preserving mapping	Partitional clustering (centroid-based)
Основополагащ източник	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.
Други названия	SOM, Kohonen map, Kohonen network, öz-örgütlemeli harita	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering
Свързани	3	3
Резюме	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.