Võrdle meetodeid

	Hierarchical Clustering	Spektraalklasterdamine
Valdkond	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	1963	2002
Looja	Ward, J. H.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Tüüp	Unsupervised clustering (agglomerative)	Graph-based clustering (spectral method)
Algallikas	Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236–244.	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.
Rööpnimetused	Hiyerarşik Kümeleme, hiyerarşik kümeleme, agglomerative clustering, hierarchical agglomerative clustering	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Seotud	4	5
Kokkuvõte	Hierarchical clustering is an unsupervised method that groups observations into nested clusters and draws the result as a dendrogram, so the number of clusters need not be fixed in advance. Its agglomerative form rests on the objective-function grouping criterion introduced by Joe Ward in 1963.	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.