Porovnat metody

	Spektrální shlukování	Analýza hlavních komponent
Obor	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning
Rok vzniku	2002	2002
Tvůrce	Ng, A. Y.; Jordan, M. I.; Weiss, Y.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
Typ	Graph-based clustering (spectral method)	Unsupervised dimensionality reduction
Původní zdroj	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.
Další názvy	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
Příbuzné	5	3
Shrnutí	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.	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.