The Local Outlier Factor (LOF) is a machine learning algorithm used for outlier detection and anomaly detection in datasets. The LOF algorithm measures the local density deviation of a data point with respect to its neighbors.
LOF is particularly useful when dealing with datasets where traditional distance-based methods might not work well due to varying densities of data points in different regions of the dataset. It's often used in scenarios such as fraud detection, network security, and industrial quality control, where identifying anomalies is crucial for maintaining the integrity of a system or process.
Steps…
Local Density Calculation: For each data point, the algorithm calculates the distance to its k-nearest neighbors, where k is a user-defined parameter.
Local Reachability Density: For each point, the reachability distance is calculated as the inverse of the average k-distance of its k-nearest neighbors.
LOF Calculation: The LOF score of a data point is computed by comparing its local reachability density with that of its neighbors.
A high LOF score suggests that a data point is an outlier. A low LOF score indicates that the point is similar to its neighbors and is part of a dense region.