Anomaly detection using fuzzy association rules

Data mining techniques are a very important tool for extracting useful knowledge from databases. Recently, some approaches have been developed for mining novel kinds of useful information, such as anomalous rules. These kinds of rules are a good technique for the recognition of normal and anomalous behaviour, that can be of interest in several area domains such as security systems, financial data analysis, network traffic flow, etc. The aim of this paper is to propose an association rule mining process for extracting the common and anomalous patterns in data that is affected by some kind of imprecision or uncertainty, obtaining information that will be meaningful and interesting for the user. This is done by mining fuzzy anomalous rules. We present a new approach for mining such rules, and we apply it to the case of detecting normal and anomalous patterns on credit data.