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	DBSCAN	Klastrowanie spektralne
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1996	2002
Twórca	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Typ	Density-based clustering algorithm	Graph-based clustering (spectral method)
Źródło pierwotne	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	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.
Inne nazwy	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Pokrewne	3	5
Podsumowanie	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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.