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	Reguły Asocjacyjne Bayesa	FP-Growth (Frequent Pattern Growth)
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1994–1995	2000
Twórca	Heckerman, D. et al.; Agrawal, R. & Srikant, R.	Jiawei Han, Jian Pei & Yiwen Yin
Typ	Probabilistic rule mining	Frequent-itemset mining algorithm
Źródło pierwotne	Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197–243.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Inne nazwy	Bayesian rule learning, probabilistic association rules, Bayesian itemset mining, BAR	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Pokrewne	6	4
Podsumowanie	Bayesian Association Rules extend classical association rule mining by placing a prior probability distribution over rules and scoring them by their posterior probability given the data. Rather than thresholding on raw support and confidence counts, this Bayesian framework naturally penalises complexity, corrects for multiple comparisons, and produces calibrated probabilistic rule strengths across transactional or categorical datasets.	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.