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	Robust HDBSCAN	Grupowanie K-średnich (K-means Clustering)	Klastrowanie spektralne
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	2015	1967 (formalized 1982)	2002
Twórca	Campello, R.J.G.B.; Moulavi, D.; Zimek, A.; Sander, J.	MacQueen, J. B.; Lloyd, S. P.	Ng, A. Y.; Jordan, M. I.; Weiss, Y.
Typ	Hierarchical density-based clustering with robust single-linkage	Partitional clustering	Graph-based clustering (spectral method)
Źródło pierwotne	Campello, R.J.G.B., Moulavi, D., Zimek, A. & Sander, J. (2015). Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data, 10(1), 5.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	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	HDBSCAN*, Robust HDBSCAN*, robust hierarchical density clustering, robust single-linkage HDBSCAN	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering
Pokrewne	4	4	5
Podsumowanie	Robust HDBSCAN (HDBSCAN*) extends the original HDBSCAN algorithm with a robust single-linkage framework that handles noise, outliers, and clusters of varying densities more reliably. Introduced by Campello et al. (2015), it converts any density-based hierarchy into a stable flat clustering while explicitly modeling noise points — without requiring the user to pre-specify the number of clusters.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.	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.