השוואת שיטות

	Local Outlier Factor (LOF)	מכונת וקטורים תומכים חד-מחלקתית (One-Class SVM)
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2000	1999–2001
הוגה השיטה	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.	Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C.
סוג	Density-based anomaly detection (unsupervised)	Anomaly / novelty detection (unsupervised)
מקור מכונן	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.
כינויים	LOF, local outlier factor, density-based outlier detection, local density deviation	OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM
קשורות	4	3
תקציר	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.