Abstract:

In order to discover abnormal data in a database, a new concept of correlated confidence between two data itemsets is proposed, and the algorithm of computing the rules for detecting abnormal data based on the metric is studied. The inferred rules are suitable for detecting discrete attribute outliers. In computing the rules, the minimum threshold of correlated confidence is determined by the frequency of 1-itemsets instead of users, and the temporal complexity of the algorithm for computing rules can be reduced by using the properties of correlated confidence. The experiment results show that the correlated rules inferred by the method for detecting abnormal data have not only high efficiency but also high precision and recall.