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| Asszociációs szabályok× | K-means klaszterezés× | |
|---|---|---|
| Tudományterület | Gépi tanulás | Gépi tanulás |
| Módszercsalád | Machine learning | Machine learning |
| Keletkezés éve≠ | 1993 | 1967 (formalized 1982) |
| Megalkotó≠ | Agrawal, R., Imielinski, T., & Swami, A. | MacQueen, J. B.; Lloyd, S. P. |
| Típus≠ | Unsupervised pattern discovery | Partitional clustering |
| Alapmű≠ | Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Alternatív nevek | market basket analysis, association rule mining, frequent itemset mining, affinity analysis | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Kapcsolódó | 4 | 4 |
| Összefoglaló≠ | Association rule learning is an unsupervised technique that discovers co-occurrence patterns — 'if X then Y' implications — within large transactional datasets. Originally formalized by Agrawal, Imielinski, and Swami (1993) for supermarket basket analysis, it is now widely applied in e-commerce recommendation, health informatics, bioinformatics, and behavioral research. | 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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