Vertaile menetelmiä
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| Päätöspuu× | FP-Growth (Frequent Pattern Growth)× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1984 | 2000 |
| Kehittäjä≠ | Breiman, Friedman, Olshen & Stone | Jiawei Han, Jian Pei & Yiwen Yin |
| Tyyppi≠ | Recursive partitioning (if-then rules) | Frequent-itemset mining algorithm |
| Alkuperäislähde≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ |
| Rinnakkaisnimet≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme |
| Liittyvät≠ | 5 | 4 |
| Tiivistelmä≠ | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. | 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. |
| ScholarGateAineisto ↗ |
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