Bandingkan kaedah

	Peraturan Persatuan Bayesian	FP-Growth (Pertumbuhan Corak Kerap)
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1994–1995	2000
Pengasas	Heckerman, D. et al.; Agrawal, R. & Srikant, R.	Jiawei Han, Jian Pei & Yiwen Yin
Jenis	Probabilistic rule mining	Frequent-itemset mining algorithm
Sumber perintis	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.
Alias	Bayesian rule learning, probabilistic association rules, Bayesian itemset mining, BAR	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Berkaitan	6	4
Ringkasan	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.