Linganisha mbinu

	Algoriti ya Ensemble Apriori	Kuimarisha
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1994 (Apriori base); ensemble extensions 2000s–2010s	1990–1997
Mwanzilishi	Agrawal, R. & Srikant, R. (Apriori base); ensemble extension by multiple researchers	Schapire, R. E.; Freund, Y.
Aina	Ensemble / Frequent Pattern Mining	Sequential ensemble (iterative reweighting)
Chanzo asilia	Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 1215, 487–499.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.
Majina mbadala	Ensemble Apriori, Ensemble Association Rule Mining, EAR mining, Distributed Apriori Ensemble	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
Zinazohusiana	5	6
Muhtasari	The Ensemble Apriori Algorithm applies ensemble principles to the classic Apriori frequent-pattern miner by running multiple Apriori instances on different data partitions or parameter settings and merging their rule sets. This approach improves coverage, reduces sensitivity to the minimum-support threshold, and scales association rule mining to larger transactional datasets.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.