Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| DBSCAN× | Isolation Forest× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 1996 | 2008 |
| Autorul original≠ | Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tip≠ | Density-based clustering algorithm | Unsupervised ensemble (random partitioning trees) |
| Sursa seminală≠ | 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 ↗ |
| Denumiri alternative | DBSCAN Kümeleme, density-based clustering, density-based spatial clustering | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Înrudite≠ | 3 | 5 |
| Rezumat≠ | 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. |
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