Comparer des méthodes

	Algorithme Apriori	Regroupement par K-moyennes
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1994	1967 (formalized 1982)
Auteur d'origine	Agrawal, R. & Srikant, R.	MacQueen, J. B.; Lloyd, S. P.
Type	Frequent itemset and association rule mining algorithm	Partitional clustering
Source fondatrice	Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Alias	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Apparentées	5	4
Résumé	The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those patterns.	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.