Linganisha mbinu

	Kanuni za Uunganisho za Bayesian	Sheria za Chama cha Semi-zilizosimamiwa
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1994–1995	2003–2010s
Mwanzilishi	Heckerman, D. et al.; Agrawal, R. & Srikant, R.	Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers)
Aina	Probabilistic rule mining	Pattern mining with partial supervision
Chanzo asilia	Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197–243.	Liu, B., Hsu, W., & Ma, Y. (2003). Integrating Classification and Association Rule Mining. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pp. 339–346.
Majina mbadala	Bayesian rule learning, probabilistic association rules, Bayesian itemset mining, BAR	semi-supervised ARM, label-guided association rule mining, constrained association rule mining, semi-supervised pattern discovery
Zinazohusiana	6	4
Muhtasari	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.	Semi-supervised association rule mining extends classical association rule learning by incorporating a small amount of labeled data alongside a larger unlabeled dataset. It uses known class information or user-provided constraints to guide the discovery of rules that are both statistically frequent and semantically meaningful, bridging unsupervised pattern mining with light supervision.