השוואת שיטות

	אשכול ספקטרלי	ניתוח רכיבים עיקריים
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2002	2002
הוגה השיטה	Ng, A. Y.; Jordan, M. I.; Weiss, Y.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
סוג	Graph-based clustering (spectral method)	Unsupervised dimensionality reduction
מקור מכונן	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.
כינויים	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform
קשורות	5	3
תקציר	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.