Discovering relationships among association rules over time

Abstract

In this thesis, we investigate the association rules from temporal dimension. We analyze the dynamic behavior of association rule over time and propose to classify rules into different categories. We also define some interesting evolution relationships of association rules over time, which might be important and useful in real-world applications. The evolution relationships reveal the relationships about the effect of the conditions on the consequent over time, which reflect the change of the underlying data. Therefore they can give the domain expert a better idea about how and why the data changes.