So sánh phương pháp

	Quy tắc kết hợp	FP-Growth (Frequent Pattern Growth)	Học trực tuyến
Lĩnh vực	Học máy	Học máy	Học máy
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	1993	2000	1958–2000s
Người khởi xướng	Agrawal, R., Imielinski, T., & Swami, A.	Jiawei Han, Jian Pei & Yiwen Yin	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Loại	Unsupervised pattern discovery	Frequent-itemset mining algorithm	Learning paradigm (sequential model update)
Công trình gốc	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.	Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Tên gọi khác	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	incremental learning, sequential learning, streaming learning, online machine learning
Liên quan	4	4	6
Tóm tắt	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.	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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.