So sánh phương pháp

	K-means bán giám sát	Học bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2001–2002	1970s–2006 (formalized)
Người khởi xướng	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Loại	Semi-supervised clustering	Learning paradigm
Công trình gốc	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
Tên gọi khác	constrained K-means, seeded K-means, partially supervised K-means, SS-K-means	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Liên quan	5	5
Tóm tắt	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.