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| DBSCAN× | t-SNE× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1996 | 2008 |
| Urheber≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | van der Maaten, L. & Hinton, G. |
| Typ≠ | Density-based clustering algorithm | Nonlinear dimensionality reduction (manifold visualization) |
| 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 ↗ | van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605. link ↗ |
| Aliasnamen | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne |
| Verwandt | 3 | 3 |
| 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. | t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality-reduction method introduced by Laurens van der Maaten and Geoffrey Hinton in 2008 that maps high-dimensional data into a 2D or 3D space for visualization. It preserves probabilistic local similarities, so points that are neighbours in the original space stay close together, revealing cluster structure and local neighbourhoods. |
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