Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| DBSCAN× | Объяснимый Isolation Forest× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1996 | 2008 / 2017 |
| Автор метода≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Liu, F. T., Ting, K. M., & Zhou, Z.-H. (Isolation Forest); Lundberg, S. M. & Lee, S.-I. (SHAP explainability layer) |
| Тип≠ | Density-based clustering algorithm | Anomaly detection with post-hoc explainability |
| 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 ↗ | Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774. link ↗ |
| Другие названия≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | XIF, Isolation Forest with SHAP, interpretable anomaly detection, explainable anomaly isolation |
| 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. | Explainable Isolation Forest combines the Isolation Forest anomaly detection algorithm with post-hoc explainability tools — most commonly SHAP (SHapley Additive exPlanations) — to not only flag anomalous observations but also reveal which features drove each anomaly score. It bridges unsupervised anomaly detection with the interpretability demands of regulated and high-stakes domains. |
| ScholarGateНабор данных ↗ |
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