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", w
![Local outlier factor - Wikipedia](https://cdn-ak-scissors.b.st-hatena.com/image/square/1c654a58bb741aab520f6495ea459aea8836fa18/height=288;version=1;width=512/https%3A%2F%2Fupload.wikimedia.org%2Fwikipedia%2Fcommons%2Fthumb%2F0%2F02%2FNeural_network_with_dark_background.png%2F1200px-Neural_network_with_dark_background.png)