방법 비교

	Robust Gaussian Mixture Model	Isolation Forest
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2000	2008
창시자	Peel, D. & McLachlan, G. J.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
유형	Probabilistic clustering / density estimation	Unsupervised ensemble (random partitioning trees)
원전	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.
별칭	Robust GMM, mixture of t-distributions, trimmed GMM, heavy-tailed mixture model	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
관련	5	5
요약	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.