Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Algoritmus Apriori× | K-means Shlukování× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1994 | 1967 (formalized 1982) |
| Tvůrce≠ | Agrawal, R. & Srikant, R. | MacQueen, J. B.; Lloyd, S. P. |
| Typ≠ | Frequent itemset and association rule mining algorithm | Partitional clustering |
| Původní zdroj≠ | 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 ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Další názvy | Apriori, frequent itemset mining, ARL-Apriori, Apriori association mining | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Příbuzné≠ | 5 | 4 |
| Shrnutí≠ | 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. | 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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