Confronta i metodi

	Robust HDBSCAN	Clustering K-means
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	2015	1967 (formalized 1982)
Ideatore	Campello, R.J.G.B.; Moulavi, D.; Zimek, A.; Sander, J.	MacQueen, J. B.; Lloyd, S. P.
Tipo	Hierarchical density-based clustering with robust single-linkage	Partitional clustering
Fonte seminale	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.
Alias	HDBSCAN*, Robust HDBSCAN*, robust hierarchical density clustering, robust single-linkage HDBSCAN	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Correlati	4	4
Sintesi	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.