Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| DBSCAN× | Одноклассовая SVM× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 1996 | 1999–2001 |
| Автор метода≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| Тип≠ | Density-based clustering algorithm | Anomaly / novelty detection (unsupervised) |
| 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 ↗ | Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ |
| Другие названия≠ | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| Связанные | 3 | 3 |
| 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. | One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available. |
| ScholarGateНабор данных ↗ |
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