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Bayesian Association Rules/Evidence
Method evidence record

Bayesian Association Rules

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.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Bayesian Association Rule Mining
Taxonomic method record · ml-model / machine-learning
  • Heckerman, D., Geiger, D., & Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20(3), 197–243. · DOI 10.1007/BF00994016
  • Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 1215, 487–499. · URL
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Claims persisted in the evidence ledger, each with its own assessment.

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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Taxonomic bucketApriori Algorithmmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketAssociation Rulesmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Gaussian Mixture Modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketBayesian Naive Bayesmachine-suggested · Relational suggestion, not evidence.Same method familyFP-Growthmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketSemi-supervised Association Rulesmachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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