Порівняння методів

	Видобування частих наборів елементів ECLAT	FP-Growth (Frequent Pattern Growth)
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	2000	2000
Автор методу	Mohammed J. Zaki	Jiawei Han, Jian Pei & Yiwen Yin
Тип	Frequent-itemset mining algorithm (vertical format)	Frequent-itemset mining algorithm
Основоположне джерело	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.
Інші назви	Eclat algorithm, vertical association mining, tidset intersection mining, ECLAT sık örüntü madenciliği	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Пов'язані	3	4
Підсумок	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.