Comparer des méthodes

	HDBSCAN	Regroupement par K-moyennes	Apprentissage auto-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2013	1967 (formalized 1982)	2018–2020
Auteur d'origine	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	MacQueen, J. B.; Lloyd, S. P.	LeCun, Y. and community (formalized ~2018–2020)
Type	Hierarchical density-based clustering	Partitional clustering	Representation learning paradigm
Source fondatrice	Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Alias	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Apparentées	3	4	3
Résumé	HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions.	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.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.