So sánh phương pháp

	K-means tự giám sát	K-means bán giám sát
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	2018	2001–2002
Người khởi xướng	Caron, M. et al. (DeepCluster framework)	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)
Loại	Self-supervised clustering	Semi-supervised clustering
Công trình gốc	Caron, M., Bojanowski, P., Joulin, A., & Douze, M. (2018). Deep Clustering for Unsupervised Learning of Visual Features. In Proceedings of the European Conference on Computer Vision (ECCV), 132–149.	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.
Tên gọi khác	self-supervised clustering with K-means, deep clustering with K-means, unsupervised K-means with pseudo-labels, SSL K-means	constrained K-means, seeded K-means, partially supervised K-means, SS-K-means
Liên quan	5	5
Tóm tắt	Self-supervised K-means is a clustering technique that combines K-means assignment with self-supervised representation learning. The model alternates between clustering unlabeled data points into K groups and using those cluster assignments as pseudo-labels to refine an underlying feature representation, yielding increasingly coherent clusters without any human-annotated ground truth.	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.