Comparer des méthodes

	Isolation Forest	Facteur d'Anomalie Locale (LOF)
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2008	2000
Auteur d'origine	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.
Type	Unsupervised ensemble (random partitioning trees)	Density-based anomaly detection (unsupervised)
Source fondatrice	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.	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.
Alias	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	LOF, local outlier factor, density-based outlier detection, local density deviation
Apparentées	5	4
Résumé	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.	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.