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	Reguły Asocjacyjne Bayesa	Reguły asocjacyjne
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	1994–1995	1993
Twórca	Heckerman, D. et al.; Agrawal, R. & Srikant, R.	Agrawal, R., Imielinski, T., & Swami, A.
Typ	Probabilistic rule mining	Unsupervised pattern discovery
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.	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.
Inne nazwy	Bayesian rule learning, probabilistic association rules, Bayesian itemset mining, BAR	market basket analysis, association rule mining, frequent itemset mining, affinity analysis
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.	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.