Enhanced mining of association rules from data cubes
DOLAP '06 Proceedings of the 9th ACM international workshop on Data warehousing and OLAP
Pruning attribute values from data cubes with diamond dicing
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
A Multiple Correspondence Analysis to Organize Data Cubes
Proceedings of the 2007 conference on Databases and Information Systems IV: Selected Papers from the Seventh International Baltic Conference DB&IS'2006
Embedded indicators to facilitate the exploration of a data cube
International Journal of Business Intelligence and Data Mining
DHCC: Divisive hierarchical clustering of categorical data
Data Mining and Knowledge Discovery
Built-in indicators to automatically detect interesting cells in a cube
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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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.