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In decision-support systems, the visual component is important for On LineAnalysis Processing (OLAP). In this paper, we propose a new approach that facesthe visualization problem due to data sparsity. We use the results of aMultiple Correspondence Analysis (MCA) to reduce the negative effect ofsparsity by organizing differently data cube cells. Our approach does notreduce sparsity, however it tries to build relevant representation spaces wherefacts are efficiently gathered. In order to evaluate our approach, we proposean homogeneity criterion based on geometric neighborhood of cells. The obtainedexperimental results have shown the efficiency of our method.

Espaces de repr\'esentation multidimensionnels d\'edi\'es \`a la visualisation