Sammenlign metoder

	Robust k-means	Spektral klyngedannelse
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1999	2002
Ophavsperson	Garcia-Escudero, L. A. & Gordaliza, A.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Type	Robust clustering algorithm	Graph-based clustering (spectral method)
Oprindelig kilde	Garcia-Escudero, L. A., & Gordaliza, A. (1999). Robustness properties of k-means and trimmed k-means. Journal of the American Statistical Association, 94(447), 956–969.	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	robust k-means clustering, trimmed k-means, outlier-resistant k-means, RKM	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Relaterede	4	5
Resumé	Robust k-means is a variant of classical k-means clustering designed to resist the influence of outliers. By trimming a specified fraction of the most extreme observations before computing cluster centers, it produces stable and meaningful partitions even when the data contain noise, contamination, or heavy-tailed distributions — situations where standard k-means breaks down.	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.