방법 비교

	Isolation Forest	t-SNE
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2008	2008
창시자	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	van der Maaten, L. & Hinton, G.
유형	Unsupervised ensemble (random partitioning trees)	Nonlinear dimensionality reduction (manifold visualization)
원전	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.
별칭	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne
관련	5	3
요약	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.	t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality-reduction method introduced by Laurens van der Maaten and Geoffrey Hinton in 2008 that maps high-dimensional data into a 2D or 3D space for visualization. It preserves probabilistic local similarities, so points that are neighbours in the original space stay close together, revealing cluster structure and local neighbourhoods.