Efficient multidimensional data representations based on multiple correspondence analysis

In the On Line Analytical Processing (OLAP) context, exploration of huge and sparse data cubes is a tedious task which does not always lead to efficient results. In this paper, we couple OLAP with the Multiple Correspondence Analysis (MCA) in order to enhance visual representations of data cubes and thus, facilitate their interpretations and analysis. We also provide a quality criterion to measure the relevance of obtained representations. The criterion is based on a geometric neighborhood concept and a similarity metric between cells of a data cube. Experimental results on real data proved the interest and the efficiency of our approach. Categories and Subject Descriptors

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Omar Boussaid, Riadh Ben Messaoud, Sabine Loudcher Rabaséda
• Keywords: Data Mining, Geometric Neighborhood Concept, KDD 2006, Multiple Correspondence Analysis, Sparse Data Cubes