Compara mètodes

	Online HDBSCAN	Clustering Espectral
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2015–2017	2002
Autor original	Campello, R. J. G. B. et al. (base); incremental extensions by Hassani, M. et al.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Tipus	Incremental hierarchical density-based clustering	Graph-based clustering (spectral method)
Font seminal	Hassani, M., Seidl, T. (2017). Using internal evaluation measures to validate the quality of diverse stream clustering algorithms. Vietnam Journal of Computer Science, 4(3), 171–183.	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.
Àlies	incremental HDBSCAN, streaming HDBSCAN, online hierarchical density clustering, dynamic HDBSCAN	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Relacionats	6	5
Resum	Online HDBSCAN extends the HDBSCAN hierarchical density-based clustering algorithm to incrementally process streaming or sequentially arriving data. Rather than rebuilding the full hierarchy from scratch with each new observation, it maintains and locally updates the mutual reachability graph, minimum spanning tree, condensed cluster tree, and stability-based cluster extraction, enabling continuous density-based clustering without full-dataset reprocessing.	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.