User preference is very important in orienting data miner, and this is the reason why these user preferences are integrated in the mining process, where they are coupled with Association Rules Mining “ARM” Algorithms to select only Association Rules “ARs” that satisfy the user’s wishes and expectations. Within this framework, several approaches were proposed to overcome some problems which persist with the traditional ARM algorithms mainly dimensionality phenomenon engendered by thresholding and the subjective choice of measures. “MDP$$_{\mathrm {REF}}$$ REF Algorithm” is one of these approaches; it prunes, filters to select the relevant ARs, while ”Rank-Sort-MDP$$_{\mathrm {REF}}$$ REF ” sorts, ranks, and stores ARs to complete the MDP$$_{\mathrm {REF}}$$ REF algorithm mining operation. Experiment result on real database showed the advantages of MDP$$_{\mathrm {REF}}$$ REF algorithm and Rank-Sort-MDP$$_{\mathrm {REF}}$$ REF algorithm over the other algorithms.