방법 비교

	자기 조직화 지도 (코호넨 지도)	t-SNE
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1982	2008
창시자	Teuvo Kohonen	van der Maaten, L. & Hinton, G.
유형	Unsupervised neural network for topology-preserving mapping	Nonlinear dimensionality reduction (manifold visualization)
원전	Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), 59–69.	van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.
별칭	SOM, Kohonen map, Kohonen network, öz-örgütlemeli harita	t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne
관련	3	3
요약	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.	t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality-reduction method introduced by Laurens van der Maaten and Geoffrey Hinton in 2008 that maps high-dimensional data into a 2D or 3D space for visualization. It preserves probabilistic local similarities, so points that are neighbours in the original space stay close together, revealing cluster structure and local neighbourhoods.