方法对比
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| 在线关联规则× | 关联规则× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1996 | 1993 |
| 提出者≠ | Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. | Agrawal, R., Imielinski, T., & Swami, A. |
| 类型≠ | Incremental / streaming pattern mining | Unsupervised pattern discovery |
| 开创性文献≠ | 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 ↗ | 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. DOI ↗ |
| 别名 | Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM | market basket analysis, association rule mining, frequent itemset mining, affinity analysis |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. |
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