Võrdle meetodeid

	Veebipõhised assotsiatsioonireeglid	Apriori algoritm	Veebipõhine õpe
Valdkond	Masinõpe	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	1996	1994	1958–2000s
Looja	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Agrawal, R. & Srikant, R.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Tüüp	Incremental / streaming pattern mining	Frequent itemset and association rule mining algorithm	Learning paradigm (sequential model update)
Algallikas	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.	Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Rööpnimetused	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	incremental learning, sequential learning, streaming learning, online machine learning
Seotud	5	5	6
Kokkuvõte	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.	The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those patterns.	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.