Interpreting the Semantics of Anomalies Based on Mutual Information in Link- Mining

This paper aims to show how mutual information can help provide a semantic interpretation of anomalies in data, characterize the anomalies, and how mutual information can help measure the information that object item X shares with another object item Y. Whilst most link mining approaches focus on predicting link type, link based object classification or object identification, this research focused on using link mining to detect anomalies and discovering links/objects among anomalies. This paper attempts to demonstrate the contribution of mutual information to interpret anomalies using a case study.