Mining unexpected multidimensional rules

Discovering unexpected rules is essential, particularly for industrial applications with marketing stakes. In this context, many works have been done for association rules. However, none of them addresses sequences. In this paper, we thus propose to discover unexpected multidimensional sequential rules in data cubes. We define the concept of multidimensional sequential rule, and then unexpectedness. We formalize these concepts and define an algorithm for mining this kind of rules. Experiments on a real data cube are reported and highlight the interest of our approach.