Compara mètodes

	K-means semi-supervisat	Aprenentatge semi-supervisat
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2001–2002	1970s–2006 (formalized)
Autor original	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Tipus	Semi-supervised clustering	Learning paradigm
Font seminal	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.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Àlies	constrained K-means, seeded K-means, partially supervised K-means, SS-K-means	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Relacionats	5	5
Resum	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.