विधियों की तुलना करें

	ऑनलाइन एसोसिएशन नियम	एसोसिएशन रूल्स	ऑनलाइन लर्निंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	1996	1993	1958–2000s
प्रवर्तक	Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y.	Agrawal, R., Imielinski, T., & Swami, A.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
प्रकार	Incremental / streaming pattern mining	Unsupervised pattern discovery	Learning paradigm (sequential model update)
मौलिक स्रोत	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., 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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
उपनाम	Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM	market basket analysis, association rule mining, frequent itemset mining, affinity analysis	incremental learning, sequential learning, streaming learning, online machine learning
संबंधित	5	4	6
सारांश	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.	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.	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.