Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| DBSCAN× | Дерево решений× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1996 | 1984 |
| Автор метода≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Breiman, Friedman, Olshen & Stone |
| Тип≠ | Density-based clustering algorithm | Recursive partitioning (if-then rules) |
| Основополагающий источник≠ | 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 ↗ |
| Другие названия≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Связанные≠ | 3 | 5 |
| Сводка≠ | 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. |
| ScholarGateНабор данных ↗ |
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