Comparar métodos

	Agrupamento K-Means	t-SNE
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1967	2008
Autor original	MacQueen, J.	van der Maaten, L. & Hinton, G.
Tipo	Partitional clustering (centroid-based)	Nonlinear dimensionality reduction (manifold visualization)
Fonte seminal	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.	van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.
Outros nomes	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne
Relacionados	3	3
Resumo	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.	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.