Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| FP-Růst (Růst častých vzorů)× | K-means Shlukování× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2000 | 1967 (formalized 1982) |
| Tvůrce≠ | Jiawei Han, Jian Pei & Yiwen Yin | MacQueen, J. B.; Lloyd, S. P. |
| Typ≠ | Frequent-itemset mining algorithm | Partitional clustering |
| Původní zdroj≠ | Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. ACM SIGMOD Record, 29(2), 1–12. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Další názvy | frequent pattern growth, FP-tree mining, FP-Growth algorithm, sık örüntü büyütme | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Příbuzné | 4 | 4 |
| Shrnutí≠ | 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. | K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis. |
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