Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Isolation Forest× | Model de amestec gaussian× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 2008 | 1977 |
| Autorul original≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Dempster, Laird & Rubin (EM algorithm) |
| Tip≠ | Unsupervised ensemble (random partitioning trees) | Probabilistic (soft) clustering — mixture model |
| Sursa seminală≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Dempster, A.P., Laird, N.M. & Rubin, D.B. (1977). Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society: Series B, 39(1), 1–22. DOI ↗ |
| Denumiri alternative≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians |
| Înrudite≠ | 5 | 4 |
| Rezumat≠ | 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. | A Gaussian Mixture Model is a probabilistic clustering method that models the data as a weighted mixture of several Gaussian distributions, fitted with the Expectation–Maximization algorithm formalized by Dempster, Laird & Rubin in 1977. It is a generalization of K-means in which each cluster can take its own shape, size, and orientation. |
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