Sammenlign metoder

	K-Means Clustering	Spektral klyngedannelse
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1967	2002
Ophavsperson	MacQueen, J.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Type	Partitional clustering (centroid-based)	Graph-based clustering (spectral method)
Oprindelig kilde	MacQueen, J. (1967). Some Methods for Classification and Analysis of Multivariate Observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.	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.
Aliasser	K-Ortalamalar Kümeleme, k-ortalamalar kümeleme, k-means, centroid clustering	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Relaterede	3	5
Resumé	K-Means Clustering is a centroid-based partitional clustering algorithm, traced to J. MacQueen in 1967, that splits data into k clusters by assigning each observation to its nearest cluster centre. It is widely used for marketing segmentation, customer grouping, and exploratory analysis.	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.