Salīdzināt metodes

	Daļēji uzraudzīti K-vidējie	Spektrālā klasterizācija
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	2001–2002	2002
Autors	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Tips	Semi-supervised clustering	Graph-based clustering (spectral method)
Pirmavots	Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584.	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.
Citi nosaukumi	constrained K-means, seeded K-means, partially supervised K-means, SS-K-means	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Saistītās	5	5
Kopsavilkums	Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full.	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.