Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| ECLAT Frequent-Itemset Mining× | FP-Рост (Рост часто встречаемых паттернов)× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления | 2000 | 2000 |
| Автор метода≠ | Mohammed J. Zaki | Jiawei Han, Jian Pei & Yiwen Yin |
| Тип≠ | Frequent-itemset mining algorithm (vertical format) | Frequent-itemset mining algorithm |
| Основополагающий источник≠ | Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390. DOI ↗ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ |
| Другие названия | Eclat algorithm, vertical association mining, tidset intersection mining, ECLAT sık örüntü madenciliği | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme |
| Связанные≠ | 3 | 4 |
| Сводка≠ | 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. | 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. |
| ScholarGateНабор данных ↗ |
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