Porovnat metody

	Lokální faktor odlehlosti (LOF)	Autoencoder	DBSCAN	Isolation Forest
Obor	Strojové učení	Hluboké učení	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning	Machine learning	Machine learning
Rok vzniku	2000	2006	1996	2008
Tvůrce	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.	Hinton, G.E. & Salakhutdinov, R.R.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Typ	Density-based anomaly detection (unsupervised)	Neural network (encoder-decoder)	Density-based clustering algorithm	Unsupervised ensemble (random partitioning trees)
Původní zdroj	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.	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.
Další názvy	LOF, local outlier factor, density-based outlier detection, local density deviation	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
Příbuzné	4	4	3	5
Shrnutí	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.	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.