Compară metode

	Clustering K-means	HDBSCAN semi-supervizat
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1967 (formalized 1982)	2017–present
Autorul original	MacQueen, J. B.; Lloyd, S. P.	McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors
Tip	Partitional clustering	Semi-supervised density-based clustering
Sursa seminală	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.
Denumiri alternative	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN
Înrudite	4	6
Rezumat	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.	Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge.