Comparer des méthodes

	K-means semi-supervisé	DBSCAN
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2001–2002	1996
Auteur d'origine	Wagstaff, K. et al. (constrained); Basu, S. et al. (seeded)	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Type	Semi-supervised clustering	Density-based clustering algorithm
Source fondatrice	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.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.
Alias	constrained K-means, seeded K-means, partially supervised K-means, SS-K-means	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Apparentées	5	3
Résumé	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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.