In decision-support systems, the visual component is important for On Line Analysis Processing (OLAP). In this paper, we propose a new approach that faces the visualization problem due to data sparsity. We use the results of a Multiple Correspondence
Analysis (MCA) to reduce the negative effect of sparsity by organizing differently data cube cells. Our approach does not reduce sparsity, however it tries to build relevant representation spaces where facts are efficiently gathered. In order to evaluate our approach, we propose an homogeneity criterion based on geometric neighborhood of cells. The obtained experimental results have shown the efficiency of our method.
