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 …

The anomaly score of each sample is called the Local Outlier Factor. It measures the local deviation of the density of a given sample with respect to its neighbors.

Jul 2, 2025 · Local outlier factor (LOF) is an algorithm used for Unsupervised outlier detection. It produces an anomaly score for outliers in the data set. It does this by measuring the local …

Aug 31, 2020 · When a point is considered as an outlier based on its local neighborhood, it is a local outlier. LOF will identify an outlier considering the density of the neighborhood.

Jun 10, 2025 · What is Local Outlier Factor (LOF)? LOF is a density-based anomaly detection algorithm used to identify data points that are significantly different from their neighbors.

The local outlier factor (LOF) algorithm detects anomalies based on the relative density of an observation with respect to the surrounding neighborhood. The algorithm finds the k -nearest …

Apr 24, 2025 · That, in essence, is what the Local Outlier Factor (LOF) is designed to detect: not just distance, but relative isolation. Why Not Just Use Distance? Traditional distance-based …

In Fig., o1 and o2 are local outliers to C1, o3 is a global outlier, but o4 is not an outlier. However, proximity-based clustering cannot find o1 and o2 are outlier (e.g., comparing with O4).

Local Outlier Factor (LOF) is a density-based unsupervised machine learning algorithm that identifies outliers by comparing the density of data points in their local neighborhoods, rather …

The Local Outlier Factor (LOF) algorithm is an unsupervised anomaly detection method which computes the local density deviation of a given data point with respect to its neighbors. It …