In order to extract interesting knowledge from large amounts of rules produced by the data mining algorithms, visual representations of association rules are increasingly used. These representations can help users to find and to validate interesting knowledge. All techniques proposed for visualisation of rules have been developed to represent an association rule as a whole without paying attention to the relations among the items that make up the antecedent and the consequent and the contribution of each one to the rule. In this paper, we propose a new visualisation representation for association rules that allows the visualisation of the items which make up the antecedent and the consequent, the contribution of each one to the rule, and the correlations between each pair of the antecedent and each pair of consequent.