Vertaile menetelmiä
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| Puoliohjautuva assosiaatiosääntöjen louhinta× | FP-Growth (Frequent Pattern Growth)× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2003–2010s | 2000 |
| Kehittäjä≠ | Liu, B.; Hsu, W.; Ma, Y. (and subsequent researchers) | Jiawei Han, Jian Pei & Yiwen Yin |
| Tyyppi≠ | Pattern mining with partial supervision | Frequent-itemset mining algorithm |
| Alkuperäislähde≠ | Liu, B., Hsu, W., & Ma, Y. (2003). Integrating Classification and Association Rule Mining. In Proceedings of the 4th IEEE International Conference on Data Mining (ICDM), pp. 339–346. link ↗ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ |
| Rinnakkaisnimet | semi-supervised ARM, label-guided association rule mining, constrained association rule mining, semi-supervised pattern discovery | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme |
| Liittyvät | 4 | 4 |
| Tiivistelmä≠ | Semi-supervised association rule mining extends classical association rule learning by incorporating a small amount of labeled data alongside a larger unlabeled dataset. It uses known class information or user-provided constraints to guide the discovery of rules that are both statistically frequent and semantically meaningful, bridging unsupervised pattern mining with light supervision. | 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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