Comparer des méthodes

	Règles d'association	FP-Growth (Frequent Pattern Growth)	Regroupement par K-moyennes
Domaine	Apprentissage automatique	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	1993	2000	1967 (formalized 1982)
Auteur d'origine	Agrawal, R., Imielinski, T., & Swami, A.	Jiawei Han, Jian Pei & Yiwen Yin	MacQueen, J. B.; Lloyd, S. P.
Type	Unsupervised pattern discovery	Frequent-itemset mining algorithm	Partitional clustering
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.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Alias	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Apparentées	4	4	4
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.	FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.	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.