Порівняння методів

	Bagging (Bootstrap Aggregating)	FP-Growth (Frequent Pattern Growth)
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	1996	2000
Автор методу	Breiman, L.	Jiawei Han, Jian Pei & Yiwen Yin
Тип	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Frequent-itemset mining algorithm
Основоположне джерело	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.
Інші назви	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme
Пов'язані	5	4
Підсумок	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.	FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets.