Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| DBSCAN× | Spectrale Clustering× | |
|---|---|---|
| Vakgebied | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 1996 | 2002 |
| Grondlegger≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Ng, A. Y.; Jordan, M. I.; Weiss, Y. |
| Type≠ | Density-based clustering algorithm | Graph-based clustering (spectral method) |
| Oorspronkelijke bron≠ | 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 ↗ | 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 ↗ |
| Aliassen≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | NJW spectral clustering, graph Laplacian clustering, normalized spectral clustering, spectral graph clustering |
| Verwant≠ | 3 | 5 |
| Samenvatting≠ | 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. | 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. |
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