เปรียบเทียบวิธี

	Isolation Forest	แบบจำลองส่วนผสมแบบเกาส์เซียน	การวิเคราะห์องค์ประกอบหลัก	Random Forest
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning	Machine learning	Machine learning
ปีกำเนิด	2008	1977	2002	2001
ผู้ริเริ่ม	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Dempster, Laird & Rubin (EM algorithm)	Jolliffe, I.T. (textbook); Pearson & Hotelling (origins)	Breiman, L.
ประเภท	Unsupervised ensemble (random partitioning trees)	Probabilistic (soft) clustering — mixture model	Unsupervised dimensionality reduction	Ensemble (bagging of decision trees)
แหล่งต้นตำรับ	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.	Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
ชื่อเรียกอื่น	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	Gaussian Karışım Modeli (GMM Kümeleme), GMM, GMM clustering, mixture of Gaussians	Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
ที่เกี่ยวข้อง	5	4	3	4
สรุป	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.	Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.