Comparer des méthodes

	DBSCAN auto-supervisé	Regroupement par K-moyennes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2018–2021	1967 (formalized 1982)
Auteur d'origine	Ester et al. (DBSCAN base); pipeline pattern established in multiple works c. 2018–2021	MacQueen, J. B.; Lloyd, S. P.
Type	Two-stage pipeline (self-supervised pre-training + density-based clustering)	Partitional clustering
Source fondatrice	Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Alias	SSL-DBSCAN, self-supervised density clustering, contrastive DBSCAN, representation-based DBSCAN	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Apparentées	5	4
Résumé	Self-supervised DBSCAN is a two-stage unsupervised pipeline that first trains a neural encoder on a pretext task — such as contrastive learning or masked reconstruction — to produce compact, semantically meaningful embeddings from unlabeled data, and then applies DBSCAN in the resulting embedding space to discover arbitrarily shaped clusters without requiring any class labels.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.