مقایسهٔ روش‌ها

	خودرمزگذار	دی‌بی‌اسکن	جنگل ایزوله (Isolation Forest)	ماشین بردار پشتیبان تک کلاسه
حوزه	یادگیری عمیق	یادگیری ماشین	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning	Machine learning	Machine learning
سال پیدایش	2006	1996	2008	1999–2001
پدیدآور	Hinton, G.E. & Salakhutdinov, R.R.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
نوع	Neural network (encoder-decoder)	Density-based clustering algorithm	Unsupervised ensemble (random partitioning trees)	Anomaly / novelty detection (unsupervised)
منبع بنیادین	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
نام‌های دیگر	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
مرتبط	4	3	5	3
خلاصه	An autoencoder is an encoder-decoder neural network, popularised by Hinton and Salakhutdinov in 2006, that compresses data into a low-dimensional latent code and then reconstructs it, enabling dimensionality reduction and anomaly detection. By learning to rebuild its own input through a narrow bottleneck, it discovers a compact representation of the data.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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.	One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available.