Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Online FP-growth× | FP-Growth (Creștere Frecventă a Pattern-urilor)× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2004 | 2000 |
| Autorul original≠ | Cheung, W. & Zaiane, O. R. | Jiawei Han, Jian Pei & Yiwen Yin |
| Tip≠ | Incremental frequent pattern mining algorithm | Frequent-itemset mining algorithm |
| Sursa seminală≠ | Cheung, W. & Zaiane, O. R. (2004). Incremental Mining of Frequent Patterns Without Candidate Generation or Support Thr esholding. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), pp. 111–118. IEEE. link ↗ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ |
| Denumiri alternative | Incremental FP-growth, Online FP-tree, stream FP-growth, OFP-growth | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme |
| Înrudite≠ | 1 | 4 |
| Rezumat≠ | Online FP-growth is an incremental extension of the FP-growth algorithm that mines frequent itemsets from continuously arriving transaction streams without rebuilding the full FP-tree from scratch. It updates an existing compact tree structure as new transactions arrive, making it suitable for real-time and high-velocity data environments where a full database scan is impractical. | 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. |
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