Relative unsupervised discretization for association rule mining

Abstract

The paper describes a context-sensitive discretization algorithm that can be used to completely discretize a numeric or mixed numeric-categorical dataset. The algorithm combines aspects of unsupervised (class-blind) and supervised methods. It was designed with a view to the problem of finding association rules or functional dependencies in complex, partly numerical data. The paper describes the algorithm and presents systematic experiments with a synthetic data set that contains a number of rather complex associations. Experiments with varying degrees of noise and "fuzziness" demonstrate the robustness of the method. An application to a large real-world dataset produced interesting preliminary results, which are currently the topic of specialized investigations.