方法对比
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| FP-Growth (频繁模式增长)× | 在线学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2000 | 1958–2000s |
| 提出者≠ | Jiawei Han, Jian Pei & Yiwen Yin | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| 类型≠ | Frequent-itemset mining algorithm | Learning paradigm (sequential model update) |
| 开创性文献≠ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| 别名 | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme | incremental learning, sequential learning, streaming learning, online machine learning |
| 相关≠ | 4 | 6 |
| 摘要≠ | FP-Growth, introduced by Jiawei Han, Jian Pei, and Yiwen Yin in 2000, mines frequent itemsets from transaction data without generating candidate sets, the costly step that slows the classic Apriori algorithm. It compresses the database into a frequent-pattern tree (FP-tree) in two scans, then grows frequent patterns recursively from that structure, making it dramatically faster than Apriori on large, dense datasets. | 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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