Порівняння методів

	Локальний коефіцієнт викидів (LOF)	DBSCAN	One-class SVM
Галузь	Машинне навчання	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning	Machine learning
Рік появи	2000	1996	1999–2001
Автор методу	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Тип	Density-based anomaly detection (unsupervised)	Density-based clustering algorithm	Anomaly / novelty detection (unsupervised)
Основоположне джерело	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.	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.	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.
Інші назви	LOF, local outlier factor, density-based outlier detection, local density deviation	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Пов'язані	4	3	3
Підсумок	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.	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.	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.