方法对比

	谱聚类	K-means聚类	主成分分析
领域	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	2002	1967 (formalized 1982)	2002
提出者	Ng, A. Y.; Jordan, M. I.; Weiss, Y.	MacQueen, J. B.; Lloyd, S. P.	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)
类型	Graph-based clustering (spectral method)	Partitional clustering	Unsupervised dimensionality reduction
开创性文献	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.
别名	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
相关	5	4	3
摘要	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.