Comparer des méthodes

	Regroupement par K-moyennes	Apprentissage semi-supervisé
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1967 (formalized 1982)	1970s–2006 (formalized)
Auteur d'origine	MacQueen, J. B.; Lloyd, S. P.	Vapnik, V. N. and others (community of researchers, 1970s–2000s)
Type	Partitional clustering	Learning paradigm
Source fondatrice	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Chapelle, O., Scholkopf, B., & Zien, A. (Eds.) (2006). Semi-Supervised Learning. MIT Press. ISBN: 978-0-262-03358-9
Alias	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	SSL, semi-supervised machine learning, transductive learning, label-efficient learning
Apparentées	4	5
Résumé	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.	Semi-supervised learning (SSL) is a machine learning paradigm that trains models using a small set of labeled examples together with a much larger pool of unlabeled data. By leveraging the structure inherent in unlabeled data, SSL achieves accuracy closer to fully supervised models while requiring far fewer costly manual labels — making it practical when labeling is expensive, slow, or resource-constrained.