Comparar métodos

	UMAP	Agrupamento K-means	Análise de Componentes Principais
Área	Aprendizado de máquina	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning	Machine learning
Ano de origem	2018	1967 (formalized 1982)	2002
Autor original	McInnes, L.; Healy, J.; Melville, J.	MacQueen, J. B.; Lloyd, S. P.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipo	Nonlinear manifold-learning dimension reduction	Partitional clustering	Unsupervised dimensionality reduction
Fonte seminal	McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Outros nomes	UMAP (Uniform Manifold Approximation and Projection), uniform manifold approximation and projection, manifold dimension reduction	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Relacionados	5	4	3
Resumo	UMAP (Uniform Manifold Approximation and Projection) is a fast, scalable nonlinear dimension-reduction method grounded in manifold-learning theory, introduced by McInnes, Healy and Melville in 2018. It compresses high-dimensional data into a low-dimensional embedding for visualisation and downstream analysis.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.