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| DBSCAN× | Drzewo decyzyjne× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 1996 | 1984 |
| Twórca≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Breiman, Friedman, Olshen & Stone |
| Typ≠ | Density-based clustering algorithm | Recursive partitioning (if-then rules) |
| Źródło pierwotne≠ | 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 ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Inne nazwy≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Pokrewne≠ | 3 | 5 |
| Podsumowanie≠ | 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. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. |
| ScholarGateZbiór danych ↗ |
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