So sánh phương pháp

	Luật kết hợp trực tuyến	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	1996	2000	1958–2000s
Người khởi xướng	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Jiawei Han, Jian Pei & Yiwen Yin	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Loại	Incremental / streaming pattern mining	Frequent-itemset mining algorithm	Learning paradigm (sequential model update)
Công trình gốc	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. (1996). Maintenance of discovered association rules in large databases: an incremental updating technique. In Proceedings of the 12th International Conference on Data Engineering (ICDE 1996), pp. 106–114. IEEE.	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	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme	incremental learning, sequential learning, streaming learning, online machine learning
Liên quan	5	4	6
Tóm tắt	Online association rule mining discovers if-then patterns (e.g., buying bread implies buying butter) from transactional data that arrives incrementally or as a stream, updating existing rules and item counts without re-scanning the entire historical database each time new records arrive.	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.