Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Isolation Forest× | Modelo de Mezcla Gaussiana× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2008 | 1977 |
| Autor original≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Dempster, Laird & Rubin (EM algorithm) |
| Tipo≠ | Unsupervised ensemble (random partitioning trees) | Probabilistic (soft) clustering — mixture model |
| Fuente seminal≠ | 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 ↗ |
| Alias≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians |
| Relacionados≠ | 5 | 4 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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