Sammenlign metoder

	Associeringsregler	Bagging (Bootstrap Aggregating)
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	1993	1996
Ophavsperson	Agrawal, R., Imielinski, T., & Swami, A.	Breiman, L.
Type	Unsupervised pattern discovery	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Oprindelig kilde	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.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Aliasser	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Relaterede	4	5
Resumé	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.	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.