Discovering Fuzzy Exception and Anomalous Rules

Abstract

Nowadays, searching for specific kind of knowledge that deviates from the usual standards is very useful in several domains such as network traffic anomalies, fraud detection, economic analysis, or medical diagnosis. Fuzzy association rules have been developed as a powerful tool for dealing with imprecision in databases (that may come from the source, i.e., imprecise measures taken by the machine, or from the human understanding of a concept) and offering a comprehensive representation of found knowledge. In this paper, we introduce the notion of fuzzy exception and fuzzy anomalous rule for the recognition of these types of deviations. The deviations are associated with the common patterns, which usually are hidden in data affected by some kind of fuzziness. We present a new approach for mining such rules based on a recently proposed model for representing and evaluating fuzzy rules. Important advantages are to obtain more understandable results and that the mining process can be parallelized. An algorithm following the proposed model is developed, and some experiments are performed in data where some numerical attributes have been fuzzified and also in some real fuzzy transactional datasets for testing the proposed algorithm. From experimentation, we have seen that the proposed fuzzy rules give some insights on the exception and anomaly detection in credit payments.