Locating anomalies using Bayesian factorizations and masks

A plethora of methods have been developed to handle anomaly detection in various application domains. This work focuses on locating anomalies inside a categorical data set without assuming any specific domain knowledge. By exploiting the conditional dependence and independence relationships among data attributes, not only can data analysts recognize the anomaly, but also locate the potentially anomalous attributes inside an anomalous instance following its masks. Masks are geometrically generated based on the factorization of the joint probability from a Bayesian network automatically learnt from the given data set.