Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| DBSCAN× | K-Nearest Neighbors explicabil× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 1996 | 1967 (KNN); 2010s (explainability extensions) |
| Autorul original≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Cover, T. & Hart, P. (KNN); XAI extensions by various authors |
| Tip≠ | Density-based clustering algorithm | Instance-based learning with explainability layer |
| Sursa seminală≠ | 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 ↗ | Cover, T. & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ |
| Denumiri alternative≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | XKNN, Interpretable KNN, Explainable KNN, Transparent K-Nearest Neighbors |
| Înrudite≠ | 3 | 4 |
| Rezumat≠ | 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. | Explainable K-Nearest Neighbors (XKNN) augments the classic KNN classifier or regressor with structured post-hoc or built-in explanation mechanisms, exposing which retrieved neighbors, which features, and which distance contributions drive each individual prediction — making the model's reasoning transparent and auditable for human decision-makers. |
| ScholarGateSet de date ↗ |
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