Compară metode

	K-means semi-supervizat	Învățare activă
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2001–2002	2009
Autorul original	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)	Burr Settles
Tip	Semi-supervised clustering	Interactive supervised learning framework
Sursa seminală	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.	Settles, B. (2009). Active learning literature survey. University of Wisconsin-Madison Computer Sciences Technical Report 1648.
Denumiri alternative	constrained K-means, seeded K-means, partially supervised K-means, SS-K-means	Query Learning, Optimal Experimental Design (ML context), Pool-Based Active Learning, Aktif Öğrenme
Înrudite	5	2
Rezumat	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.	Active learning is an iterative machine-learning paradigm in which a learning algorithm selectively queries an oracle — typically a human annotator — for labels on the most informative unlabeled examples. Formalized by Burr Settles in his seminal 2009 literature survey, active learning addresses the practical bottleneck of annotation cost by achieving high model accuracy with far fewer labeled examples than passive supervised learning requires.