Evaluation of a MCA-based approach to organize data cubes

On Line Analysis Processing (OLAP) is a technology basically created to provide users with tools in order to explore and navigate into data cubes. Unfortunately, in huge and sparse data volumes, exploration becomes a tedious task and the simple user’s intuition or experience does not always lead to efficient results. In this paper, we propose to exploit the results of the Multiple Correspondence Analysis (MCA) in order to enhance a data cube representation. Our approach address the issues of organizing data in an interesting way and detecting relevant facts. We also treat the problem of evaluating the quality of data representation in a multidimensional space. For this, we propose a new criterion to measure the relevance of data representations. This criterion is based on the concept of geometric neighborhood and similarity between cells of a data cube. The experimental results we led on real data samples have shown the interest and the efficiency of our approach. Categories and Sub...

• Type: Conference
• Year: 2005
• Where: CIKM
• Authors: Riadh Ben Messaoud, Omar Boussaid, Sabine Rabaséda
• Keywords: CIKM 2005, Data Cube, Data Representations, Sparse Data Volumes