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| Algorytm Apriori× | FP-Growth (Frequent Pattern Growth)× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1994 | 2000 |
| Twórca≠ | Agrawal, R. & Srikant, R. | Jiawei Han, Jian Pei & Yiwen Yin |
| Typ≠ | Frequent itemset and association rule mining algorithm | Frequent-itemset mining algorithm |
| Źródło pierwotne≠ | Agrawal, R. & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), 487–499. link ↗ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ |
| Inne nazwy | Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | The Apriori algorithm, introduced by Agrawal and Srikant in 1994, is the foundational method for discovering frequent itemsets and association rules in transactional databases. It uses a breadth-first, level-wise search guided by the anti-monotone property of support to efficiently enumerate all item combinations that co-occur above a user-set minimum threshold, then extracts interpretable if-then rules from those patterns. | 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. |
| ScholarGateZbiór danych ↗ |
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