So sánh phương pháp

	Local Outlier Factor (LOF)	Bộ tự mã hóa	Isolation Forest
Lĩnh vực	Học máy	Học sâu	Học máy
Họ	Machine learning	Machine learning	Machine learning
Năm ra đời	2000	2006	2008
Người khởi xướng	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.	Hinton, G.E. & Salakhutdinov, R.R.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Loại	Density-based anomaly detection (unsupervised)	Neural network (encoder-decoder)	Unsupervised ensemble (random partitioning trees)
Công trình gốc	Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104.	Hinton, G.E. & Salakhutdinov, R.R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science, 313(5786), 504–507.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Tên gọi khác	LOF, local outlier factor, density-based outlier detection, local density deviation	Otokodlayıcı (Autoencoder), otokodlayıcı, auto-encoder, encoder-decoder network	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Liên quan	4	4	5
Tóm tắt	Local Outlier Factor (LOF) is a density-based, unsupervised anomaly detection algorithm introduced by Breunig, Kriegel, Ng, and Sander in 2000. It assigns each data point a continuous outlier score that quantifies how isolated that point is relative to its local neighborhood, enabling detection of anomalies that global methods miss because they blend into dense clusters elsewhere in the space.	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.	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.