Nowadays, decision support systems are evolving in order to handle complex data. Some recent works have shown the interest of combining on-line analysis processing (OLAP) and data mining. We think that coupling OLAP and data mining would provide excellent solutions to treat complex data. To do that, we propose an enhanced OLAP operator based on the agglomerative hierarchical clustering (AHC). The here proposed operator, called  OpAC  (Operator for Aggregation by Clustering) is able to provide significant aggregates of facts refereed to complex objects. We complete this operator with a tool allowing the user to evaluate the best partition from the AHC results corresponding to the most interesting aggregates of facts.
['Riadh Ben Messaoud', 'Omar Boussaid', 'Sabine Loudcher Rabaséda']
A new OLAP aggregation based on the AHC technique