Σύγκριση μεθόδων

	Τοπικός Παράγοντας Εκτός Τροχιάς (LOF)	DBSCAN
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2000	1996
Δημιουργός	Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Τύπος	Density-based anomaly detection (unsupervised)	Density-based clustering algorithm
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	LOF, local outlier factor, density-based outlier detection, local density deviation	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Συναφείς	4	3
Σύνοψη	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.