Identifying temporal trajectories of association rules with fuzzy descriptions

We propose a novel postprocessing technique for identifying sets of association rules that expose a user-specified temporal development. We explicitly do not use a learning approach that requires the database to be subdivided into time frames. Instead, a global probabilistic learning method is used for induction. The resulting association rules are then matched against a set of fuzzy concepts. These concepts comprise user-built linguistic propositions that describe the evolution of rules that might be considered interesting. The proposed technique is evaluated on a real-world data set. To present the results, we introduce a modified rule visualization along the way that is an extension of our previous work.