Methoden vergleichen

	Spektrale Clusteranalyse	K-means Clustering
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2002	1967 (formalized 1982)
Urheber	Ng, A. Y.; Jordan, M. I.; Weiss, Y.	MacQueen, J. B.; Lloyd, S. P.
Typ	Graph-based clustering (spectral method)	Partitional clustering
Wegweisende Quelle	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.
Aliasnamen	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Verwandt	5	4
Zusammenfassung	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.