Krahasoni metodat

	Faktori Lokal i Anomalive (LOF)	DBSCAN	Isolation Forest
Fusha	Mësimi i makinës	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning	Machine learning
Viti i origjinës	2000	1996	2008
Krijuesi	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Liu, F.T., Ting, K.M. & Zhou, Z.-H.
Lloji	Density-based anomaly detection (unsupervised)	Density-based clustering algorithm	Unsupervised ensemble (random partitioning trees)
Burimi themelues	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.	Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422.
Emërtime të tjera	LOF, local outlier factor, density-based outlier detection, local density deviation	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection
Të lidhura	4	3	5
Përmbledhja	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.	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.