Query-Driven Knowledge Discovery via OLAP manipulations

We study KDD (Knowledge Discovery in Databases) processes on OLAP (multidimensional and multilevel) 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 representation and manipulation, and argue that such a language fits well for writing KDD queries on multidimensional and multilevel 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. Keywords : association rules, OLAP, query language

• Type: Conference
• Year: 2001
• Where: BDA
• Authors: Jean-François Boulicaut, Patrick Marcel, Christophe Rigotti
• Keywords: Association Rule Mining, Association Rules, BDA 2001, BDA 2007, Multidimensional