Compară metode

	Isolation Forest	Factorul local de aberație (LOF)
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2008	2000
Autorul original	Liu, F.T., Ting, K.M. & Zhou, Z.-H.	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.
Tip	Unsupervised ensemble (random partitioning trees)	Density-based anomaly detection (unsupervised)
Sursa seminală	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.
Denumiri alternative	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection	LOF, local outlier factor, density-based outlier detection, local density deviation
Înrudite	5	4
Rezumat	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.