Local Outlier Factor (LOF) is an anomaly detection algorithm presented as "LOF: Identifying Density-based Local Outliers" by Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng and Jörg Sander[1]. The key idea of LOF is comparing the local density of a point's neighborhood with the local density of its neighbors.
E. Keogh, S. Lonardi, and C. Ratanamahatana. KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, page 206--215. New York, NY, USA, ACM, (2004)