Comparer des méthodes

	Facteur d'Anomalie Locale (LOF)	SVM à une classe
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2000	1999–2001
Auteur d'origine	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
Type	Density-based anomaly detection (unsupervised)	Anomaly / novelty detection (unsupervised)
Source fondatrice	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.	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.
Alias	LOF, local outlier factor, density-based outlier detection, local density deviation	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
Apparentées	4	3
Résumé	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.	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.