In anomaly detection, the local outlier factor, or LOF, is an algorithm proposed by Markus Breunig, Hans-Peter Kriegel, Raymond Ng and Jorg Sander in 2000 for finding anomalous data points by measuring the local deviation of a given point relative to its neighbors. It shares concepts such as core distance and reachability distance, used for local density estimation, with the DBSCAN and OPTICS clustering algorithms.
Connections
In Field
Source Local outlier factor (Wikipedia)
Sources
Local outlier factor (Wikipedia)
In Field: Algorithms and Complexity Theory, Lead
In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M.
In Group: Outlier Detection Algorithms, Lead
In anomaly detection, the local outlier factor (LOF) is an algorithm proposed by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander in 2000 for finding anomalous data points by measuring the local deviation of a given data point with respect to its neighbours.
View the SourceReader Challenges (0)
No disputes yet. Spotted an error or a better source? Open the first one.
Sign in to dispute this or suggest a correction.