Compară metode

	HDBSCAN semi-supervizat	K-means semi-supervizat
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2017–present	2001–2002
Autorul original	McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)
Tip	Semi-supervised density-based clustering	Semi-supervised clustering
Sursa seminală	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.	Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained K-means Clustering with Background Knowledge. In Proceedings of the 18th International Conference on Machine Learning (ICML 2001), pp. 577–584.
Denumiri alternative	Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN	constrained K-means, seeded K-means, partially supervised K-means, SS-K-means
Înrudite	6	5
Rezumat	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.	Semi-supervised K-means extends standard K-means clustering by incorporating partial supervision — either a small set of labeled seed points or pairwise must-link and cannot-link constraints — to guide cluster formation. It bridges unsupervised clustering and fully supervised classification, enabling more meaningful clusters when labels are scarce but costly to obtain in full.