Comparar métodos

	K-means auto-supervisionado	K-means semi-supervisionado
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	2018	2001–2002
Autor original	Caron, M. et al. (DeepCluster framework)	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)
Tipo	Self-supervised clustering	Semi-supervised clustering
Fonte seminal	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.
Outros nomes	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
Relacionados	5	5
Resumo	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.