  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.
