Comparer des méthodes

	Règles d'association	Regroupement par K-moyennes	Ensemble par vote
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1993	1967 (formalized 1982)	1990s–2004
Auteur d'origine	Agrawal, R., Imielinski, T., & Swami, A.	MacQueen, J. B.; Lloyd, S. P.	Lam & Suen; Kuncheva, L. I. (systematic treatment)
Type	Unsupervised pattern discovery	Partitional clustering	Ensemble (combination of multiple classifiers by vote)
Source fondatrice	Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	Kuncheva, L. I. (2004). Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience. ISBN: 978-0-471-21078-8
Alias	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	majority voting classifier, hard voting, soft voting ensemble, plurality voting ensemble
Apparentées	4	4	5
Résumé	Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.	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.	A voting ensemble trains several diverse classifiers independently and combines their predictions by a vote: hard voting picks the class chosen by the most models, while soft voting averages their class-probability estimates, optionally with per-model weights. The combination usually outperforms any individual member, and requires no additional training after the base models are fitted.