Сравнение на методи

	Isolation Forest	Гаусов смесен модел
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2008	1977
Създател	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Dempster, Laird & Rubin (EM algorithm)
Тип	Unsupervised ensemble (random partitioning trees)	Probabilistic (soft) clustering — mixture model
Основополагащ източник	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	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.
Други названия	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians
Свързани	5	4
Резюме	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.