Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Online K-means× | DBSCAN× | |
|---|---|---|
| Obor | Strojové učení | Strojové učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 1967 (online update rule); 2010 (mini-batch variant) | 1996 |
| Tvůrce≠ | MacQueen, J. (batch); Sculley, D. (mini-batch web-scale variant) | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Typ≠ | Unsupervised clustering (online/streaming) | Density-based clustering algorithm |
| Původní zdroj≠ | MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Vol. 1, pp. 281–297. University of California Press. link ↗ | 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 ↗ |
| Další názvy≠ | sequential k-means, streaming k-means, incremental k-means, online clustering | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Příbuzné≠ | 4 | 3 |
| Shrnutí≠ | Online K-means is a streaming variant of the classical K-means algorithm that updates cluster centroids one observation at a time — or in small mini-batches — without storing the entire dataset in memory. It is particularly suited to large-scale, real-time, or continuously arriving data where batch recomputation would be too slow or impractical. | 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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