The huge number of association rules represent the main obstacle that a decision maker faces. In order to bypass this obstacle, an efficient selection of rules must be performed. Since selection is necessarily based on evaluation, many interestingness measures have been proposed. However, the abundance of these measures caused a new problem which is the heterogeneity of the evaluation results and this created confusion to the decision. In this scope, we propose a novel approach to discover interesting association rules without favouring or excluding any measure by adopting the notion of dominance between rules. Our approach bypasses the problem of measure heterogeneity and find a compromise between their evaluations and also bypasses another non-trivial problem which is the threshold value specification.
