FastLOF: An Expectation-Maximization based Local Outlier Detection Algorithm

Unsupervised anomaly detection techniques are becoming more and more important in a variety of application domains such as network intrusion detection, fraud detection and misuse detection. Today, unsupervised anomaly detection techniques are mainly based on quadratic complexity making it almost impossible to apply them on very large data sets. In this paper, an Expectation-Maximization algorithm is proposed which computes the Local Outlier Factor (LOF) incrementally and up to 80% faster than the standard method. Another advantage of FastLOF is that intermediate results can be used by a system already during computation. Evaluation on real world data sets reveal that FastLOF performs comparable to the best outlier detection algorithms although being significantly faster.