Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Локальный фактор выбросов (Local Outlier Factor, LOF)× | DBSCAN× | Isolation Forest× | |
|---|---|---|---|
| Область | Машинное обучение | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning | Machine learning |
| Год появления≠ | 2000 | 1996 | 2008 |
| Автор метода≠ | Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Тип≠ | Density-based anomaly detection (unsupervised) | Density-based clustering algorithm | Unsupervised ensemble (random partitioning trees) |
| Основополагающий источник≠ | Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104. DOI ↗ | 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 ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Другие названия≠ | LOF, local outlier factor, density-based outlier detection, local density deviation | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Связанные≠ | 4 | 3 | 5 |
| Сводка≠ | Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space. | 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. | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. |
| ScholarGateНабор данных ↗ |
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