Comparer des méthodes

	ECLAT : Extraction de sous-ensembles d'items fréquents	FP-Growth (Frequent Pattern Growth)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000	2000
Auteur d'origine	Mohammed J. Zaki	Jiawei Han, Jian Pei & Yiwen Yin
Type	Frequent-itemset mining algorithm (vertical format)	Frequent-itemset mining algorithm
Source fondatrice	Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Alias	Eclat algorithm, vertical association mining, tidset intersection mining, ECLAT sık örüntü madenciliği	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Apparentées	3	4
Résumé	ECLAT, introduced by Mohammed Zaki in 2000, mines frequent itemsets using a vertical data representation: instead of scanning transactions, it stores for each item the set of transaction IDs (a tidset) that contain it, and computes the support of any itemset by intersecting tidsets. This depth-first, intersection-based approach is fast and memory-efficient, an alternative to Apriori's horizontal scans and FP-Growth's tree.	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.