השוואת שיטות

	אלגוריתם Apriori	למידה מקוונת
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1994	1958–2000s
הוגה השיטה	Agrawal, R. & Srikant, R.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
סוג	Frequent itemset and association rule mining algorithm	Learning paradigm (sequential model update)
מקור מכונן	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.
כינויים	Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining	incremental learning, sequential learning, streaming learning, online machine learning
קשורות	5	6
תקציר	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.