Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Spektrālā klasterizācija× | DBSCAN× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2002 | 1996 |
| Autors≠ | Ng, A. Y.; Jordan, M. I.; Weiss, Y. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. |
| Tips≠ | Graph-based clustering (spectral method) | Density-based clustering algorithm |
| Pirmavots≠ | Ng, A. Y., Jordan, M. I., & Weiss, Y. (2002). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849–856. 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 ↗ |
| Citi nosaukumi≠ | NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering |
| Saistītās≠ | 5 | 3 |
| Kopsavilkums≠ | Spectral Clustering is a graph-based unsupervised learning algorithm, formalized by Ng, Jordan, and Weiss in 2002, that maps data points into a low-dimensional eigenspace derived from the similarity graph's Laplacian before applying k-means. This spectral embedding makes it possible to recover clusters of arbitrary shape — rings, crescents, interleaved spirals — that Euclidean distance-based methods consistently fail to separate. | 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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