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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.^[1]

LOF shares some concepts with DBSCAN and OPTICS such as the concepts of "core distance" and "reachability distance", which are used for local density estimation.^[2]

^ Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD. pp. 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4.
^ Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. R. (1999). "OPTICS-OF: Identifying Local Outliers" (PDF). Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science. Vol. 1704. pp. 262–270. doi:10.1007/978-3-540-48247-5_28. ISBN 978-3-540-66490-1.

Local outlier factor