方法对比
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| 在线关联规则× | 半监督关联规则× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 1996 | 2003–2010s |
| 提出者≠ | Cheung, D. W., Han, J., Ng, V. T., & Wong, C. Y. | Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers) |
| 类型≠ | Incremental / streaming pattern mining | Pattern mining with partial supervision |
| 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 ↗ | Liu, B., Hsu, W., & Ma, Y. (2003). Integrating Classification and Association Rule Mining. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pp. 339–346. link ↗ |
| 别名 | Incremental association rule mining, Streaming association rules, Online ARM, Incremental ARM | semi-supervised ARM, label-guided association rule mining, constrained association rule mining, semi-supervised pattern discovery |
| 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. | Semi-supervised association rule mining extends classical association rule learning by incorporating a small amount of labeled data alongside a larger unlabeled dataset. It uses known class information or user-provided constraints to guide the discovery of rules that are both statistically frequent and semantically meaningful, bridging unsupervised pattern mining with light supervision. |
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