Linganisha mbinu

	K-means Nusu-Simamiwa	Ukusanyaji wa Kikundi kwa Njia ya Spektra (Spectral Clustering)
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	2001–2002	2002
Mwanzilishi	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Aina	Semi-supervised clustering	Graph-based clustering (spectral method)
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	5	5
Muhtasari	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.