  The increasing growth of databases raises an urgent need for more accurate
methods to better understand the stored data. In this scope, association rules
were extensively used for the analysis and the comprehension of huge amounts of
data. However, the number of generated rules is too large to be efficiently
analyzed and explored in any further process. Association rules selection is a
classical topic to address this issue, yet, new innovated approaches are
required in order to provide help to decision makers. Hence, many interesting-
ness measures have been defined to statistically evaluate and filter the
association rules. However, these measures present two major problems. On the
one hand, they do not allow eliminating irrelevant rules, on the other hand,
their abun- dance leads to the heterogeneity of the evaluation results which
leads to confusion in decision making. In this paper, we propose a two-winged
approach to select statistically in- teresting and semantically incomparable
rules. Our statis- tical selection helps discovering interesting association
rules without favoring or excluding any measure. The semantic comparability
helps to decide if the considered association rules are semantically related
i.e comparable. The outcomes of our experiments on real datasets show promising
results in terms of reduction in the number of rules.
