Query driven knowledge discovery in multidimensional data
Jean‐François Boulicaut, Patrick Marcel, Christophe Rigotti · 1999
We study KDD (Knowledge Discovery in Databases) processes on multidimensional data from a query point of view. Focusing on association rule mining, we consider typical queries to cope with the pre-processing of multidimensional data and the post-processing of the discovered patterns as well. We use a model and a rule-based language stemming from the OLAP multidimensional representation, and demonstrate that such a language fits well for writing KDD queries on multidimensional data. Using an homogeneous data model and our language for expressing queries at every phase of the process appears as a valuable step towards a better understanding of interactivity during the whole process.