What is LOF algorithm?
The LOF (Local Outlier Factor) algorithm is a popular unsupervised machine learning technique used for outlier detection. It measures the local deviation of a data point with respect to its neighbors to determine its degree of anomaly. LOF calculates the density of each data point within its local neighborhood and compares it to the density of its neighboring points. If a point has a significantly lower density than its neighbors, it is considered an outlier. LOF offers advantages such as being able to detect outliers in high dimensional data and is robust against varying cluster densities. It is commonly used in anomaly detection, fraud detection, and data preprocessing in various domains.
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