Résumé
Discovering unexpected rules is essential, particularly for in- dustrial applications with marketing stakes. In this context, many works have been done for association rules. How- ever, non of them address sequences. In this paper, we thus propose to discover unexpected multidimensional sequential rules in data cubes. We define the concept of multidimen- sional sequential rule, and then unexpectedness. We formal- ize 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.