Sammenlign metoder

	FP-Growth (Frequent Pattern Growth)	ECLAT Frekvent-Elementmengde-gruvedrift
Fagfelt	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Opprinnelsesår	2000	2000
Opphavsperson	Jiawei Han, Jian Pei & Yiwen Yin	Mohammed J. Zaki
Type	Frequent-itemset mining algorithm	Frequent-itemset mining algorithm (vertical format)
Opprinnelig kilde	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390.
Alias	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	Eclat algorithm, vertical association mining, tidset intersection mining, ECLAT sık örüntü madenciliği
Relaterte	4	3
Sammendrag	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.	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.