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| Reguły asocjacyjne online× | Uczenie online× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1996 | 1958–2000s |
| Twórca≠ | Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Typ≠ | Incremental / streaming pattern mining | Learning paradigm (sequential model update) |
| Źródło pierwotne≠ | 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. link ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Inne nazwy | Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM | incremental learning, sequential learning, streaming learning, online machine learning |
| Pokrewne≠ | 5 | 6 |
| Podsumowanie≠ | 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. | 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. |
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