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| k-means robust× | DBSCAN× | |
|---|---|---|
| Camp | Aprenentatge automàtic | Aprenentatge automàtic |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 1999 | 1996 |
| Autor original≠ | Garcia-Escudero, L. A. & Gordaliza, A. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Tipus≠ | Robust clustering algorithm | Density-based clustering algorithm |
| Font seminal≠ | 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 ↗ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231. link ↗ |
| Àlies≠ | robust k-means clustering, trimmed k-means, outlier-resistant k-means, RKM | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Relacionats≠ | 4 | 3 |
| Resum≠ | 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. | DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes. |
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