Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Robust k-means× | K-means Shlukování× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1999 | 1967 (formalized 1982) |
| Tvůrce≠ | Garcia-Escudero, L. A. & Gordaliza, A. | MacQueen, J. B.; Lloyd, S. P. |
| Typ≠ | Robust clustering algorithm | Partitional clustering |
| Původní zdroj≠ | Garcia-Escudero, L. A., & Gordaliza, A. (1999). Robustness properties of k-means and trimmed k-means. Journal of the American Statistical Association, 94(447), 956–969. DOI ↗ | Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137. DOI ↗ |
| Další názvy | robust k-means clustering, trimmed k-means, outlier-resistant k-means, RKM | k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means |
| Příbuzné | 4 | 4 |
| Shrnutí≠ | Robust k-means is a variant of classical k-means clustering designed to resist the influence of outliers. By trimming a specified fraction of the most extreme observations before computing cluster centers, it produces stable and meaningful partitions even when the data contain noise, contamination, or heavy-tailed distributions — situations where standard k-means breaks down. | 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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