Porovnať metódy

	Algoritmus Apriori	Boosting	FP-Growth (rast častých vzorov)
Odbor	Strojové učenie	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	1994	1990–1997	2000
Tvorca	Agrawal, R. & Srikant, R.	Schapire, R. E.; Freund, Y.	Jiawei Han, Jian Pei & Yiwen Yin
Typ	Frequent itemset and association rule mining algorithm	Sequential ensemble (iterative reweighting)	Frequent-itemset mining algorithm
Pôvodný zdroj	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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Ďalšie názvy	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Príbuzné	5	6	4
Zhrnutie	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.