  Several researchers have explored the temporal aspect of association rules
mining. In this paper, we focus on the cyclic association rules, in order to
discover correlations among items characterized by regular cyclic variation
overtime. The overview of the state of the art has revealed the drawbacks of
proposed algorithm literatures, namely the excessive number of generated rules
which are not meeting the expert's expectations. To overcome these
restrictions, we have introduced our approach dedicated to generate the cyclic
association rules under constraints through a new method called
Constraint-Based Cyclic Association Rules CBCAR. The carried out experiments
underline the usefulness and the performance of our new approach.
