Fast and Reliable Anomaly Detection in Categorical Data
Detecting anomalies and irregularities in data is an important task, and numerous applications exist where anomaly detection is vital, e.g. detecting network intrusion, credit card fraud, insurance claim fraud, and so on. Spotting anomalies in large multi-dimensional databases is a crucial task with many applications in finance, health care, security, etc. The authors introduce COMPREX, a new approach for identifying anomalies using pattern-based compression. Informally, their method finds a collection of dictionaries that describe the norm of a database succinctly, and subsequently flags those points dissimilar to the norm - with high compression cost - as anomalies.