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

TL;DR

This paper introduces a novel visualization approach for decision-support systems that uses Multiple Correspondence Analysis to organize data cube cells, improving data representation despite sparsity.

Contribution

The paper presents a new method leveraging MCA to create meaningful representation spaces for sparse data cubes, enhancing visualization effectiveness.

Findings

The approach effectively organizes sparse data cubes.

Experimental results demonstrate improved data grouping.

The method maintains data integrity while reducing sparsity effects.

Abstract

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.