Compară metode

	Reguli de Asociere Bayesiane	FP-Growth (Creștere Frecventă a Pattern-urilor)
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1994–1995	2000
Autorul original	Heckerman, D. et al.; Agrawal, R. & Srikant, R.	Jiawei Han, Jian Pei & Yiwen Yin
Tip	Probabilistic rule mining	Frequent-itemset mining algorithm
Sursa seminală	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.
Denumiri alternative	Bayesian rule learning, probabilistic association rules, Bayesian itemset mining, BAR	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Înrudite	6	4
Rezumat	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.