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| Reguły asocjacyjne× | Bagging (agregacja bootstrapowa)× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1993 | 1996 |
| Twórca≠ | Agrawal, R., Imielinski, T., & Swami, A. | Breiman, L. |
| Typ≠ | Unsupervised pattern discovery | Ensemble meta-algorithm (variance reduction via bootstrap aggregation) |
| Źródło pierwotne≠ | 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. DOI ↗ | Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140. DOI ↗ |
| Inne nazwy≠ | market basket analysis, association rule mining, frequent itemset mining, affinity analysis | Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | 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. |
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