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	Apriori-algoritmen	Associeringsregler	FP-Growth (Frequent Pattern Growth)
Ämnesområde	Maskininlärning	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning	Machine learning
Ursprungsår	1994	1993	2000
Upphovsperson	Agrawal, R. & Srikant, R.	Agrawal, R., Imielinski, T., & Swami, A.	Jiawei Han, Jian Pei & Yiwen Yin
Typ	Frequent itemset and association rule mining algorithm	Unsupervised pattern discovery	Frequent-itemset mining algorithm
Ursprungskälla	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.	Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Alias	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Närliggande	5	4	4
Sammanfattning	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.	Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research.	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.