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| DBSCAN× | UMAP× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1996 | 2018 |
| Urheber≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | McInnes, L.; Healy, J.; Melville, J. |
| Typ≠ | Density-based clustering algorithm | Nonlinear manifold-learning dimension reduction |
| Wegweisende Quelle≠ | 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 ↗ | McInnes, L., Healy, J. & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426. link ↗ |
| Aliasnamen | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | UMAP (Uniform Manifold Approximation and Projection), uniform manifold approximation and projection, manifold dimension reduction |
| Verwandt≠ | 3 | 5 |
| Zusammenfassung≠ | 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. | UMAP (Uniform Manifold Approximation and Projection) is a fast, scalable nonlinear dimension-reduction method grounded in manifold-learning theory, introduced by McInnes, Healy and Melville in 2018. It compresses high-dimensional data into a low-dimensional embedding for visualisation and downstream analysis. |
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