Sammenlign metoder

	Ensemble Association Rules	Bagging (Bootstrap Aggregating)
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	late 1990s–2000s	1996
Ophavsperson	Various (applied ensemble philosophy from Breiman and others to association rule mining)	Breiman, L.
Type	Ensemble meta-learning over association rule learners	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)
Oprindelig kilde	Domingos, P. (1999). MetaCost: A general method for making classifiers cost-sensitive. Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 155–164.	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.
Aliasser	Ensemble ARM, aggregated association rules, combined frequent-pattern mining, multi-run association rule learning	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor
Relaterede	6	5
Resumé	Ensemble Association Rules applies ensemble learning principles to association rule mining: multiple rule sets are discovered from different data subsamples or with varied parameters, then merged and weighted to produce a more stable and complete set of co-occurrence patterns. The approach reduces sensitivity to support and confidence threshold choices and improves robustness on noisy transactional data.	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.