Methoden vergleichen

	Robustes Gaußsches Mischmodell	Isolation Forest
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2000	2008
Urheber	Peel, D. & McLachlan, G. J.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Typ	Probabilistic clustering / density estimation	Unsupervised ensemble (random partitioning trees)
Wegweisende Quelle	Peel, D. & McLachlan, G. J. (2000). Robust mixture modelling using the t distribution. Statistics and Computing, 10(4), 339–348.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Aliasnamen	Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Verwandt	5	5
Zusammenfassung	Robust Gaussian Mixture Model replaces the standard Gaussian components with heavier-tailed distributions — most commonly Student's t-distributions — or incorporates trimming and down-weighting of outliers within the EM framework. The result is a probabilistic clustering and density-estimation method that assigns genuinely anomalous points less influence on component parameters, preventing outliers from distorting cluster shapes or positions.	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.