Usporedite metode

	Algoritam Apriori	Bagging (Bootstrap Aggregating)	FP-Rast (Rast čestih obrazaca)
Područje	Strojno učenje	Strojno učenje	Strojno učenje
Obitelj	Machine learning	Machine learning	Machine learning
Godina nastanka	1994	1996	2000
Tvorac	Agrawal, R. & Srikant, R.	Breiman, L.	Jiawei Han, Jian Pei & Yiwen Yin
Vrsta	Frequent itemset and association rule mining algorithm	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Frequent-itemset mining algorithm
Temeljni izvor	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.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Drugi nazivi	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Srodne	5	5	4
Sažetak	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.