Comparar métodos

	Agrupamento Espectral	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	2002	1967 (formalized 1982)	2002
Autor original	Ng, A. Y.; Jordan, M. I.; Weiss, Y.	MacQueen, J. B.; Lloyd, S. P.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Tipo	Graph-based clustering (spectral method)	Partitional clustering	Unsupervised dimensionality reduction
Fonte seminal	Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856.	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	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering	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	Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate.	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.