Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| FP-Growth (biežo kopu augšana)× | ECLAT bieži sastopamu kopu ieguve× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads | 2000 | 2000 |
| Autors≠ | Jiawei Han, Jian Pei & Yiwen Yin | Mohammed J. Zaki |
| Tips≠ | Frequent-itemset mining algorithm | Frequent-itemset mining algorithm (vertical format) |
| Pirmavots≠ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ | Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390. DOI ↗ |
| Citi nosaukumi | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme | Eclat algorithm, vertical association mining, tidset intersection mining, ECLAT sık örüntü madenciliği |
| Saistītās≠ | 4 | 3 |
| Kopsavilkums≠ | 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. | ECLAT, introduced by Mohammed Zaki in 2000, mines frequent itemsets using a vertical data representation: instead of scanning transactions, it stores for each item the set of transaction IDs (a tidset) that contain it, and computes the support of any itemset by intersecting tidsets. This depth-first, intersection-based approach is fast and memory-efficient, an alternative to Apriori's horizontal scans and FP-Growth's tree. |
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