  The online analytical processing (OLAP) does not provide any explanation of
correlations discovered between data. Thus, the coupling of OLAP and data
mining, especially association rules, is considered as an efficient solution to
this problem. In this context, we mainly focus on a particular class of
association rules which is the cyclic association rules. These rules aimed to
discover patterns that display regular variation over user-defined intervals.
Generally,the generated patterns do not take an advantage from the
specificities of the multidimensional context namely, the consideration of the
measures and their aggregations. In this paper, we introduce a novel method for
extracting cyclic association rules from measures, and we redefine the
evaluation metrics of association rules quality inspired of the temporal
summarizability of measures concept through the integration of appropriate
aggregation functions. To prove the usefulness of our approach, we conduct an
empirical study on a real data warehouse.
