Toward Integrating Data Warehousing with Data Mining Techniques
Rokia Missaoui (Universite du Quebec en Outaouais, Canada), Ganaël Jatteau (Université du Québec en Outaouais, Canada), Ameur Boujenoui (University of Ottawa, Canada) and Sami Naouali (Université du Québec en Outaouais, Canada)
Copyright: © 2008
In this paper, we present alternatives for coupling data warehousing and data mining techniques so that they can benefit from each other’s advances for the ultimate objective of efficiently providing a flexible answer to data mining queries addressed either to a bidimensional (relational) or a multidimensional database. In particular, we investigate two techniques: (i) the first one exploits concept lattices for generating frequent closed itemsets, clusters and association rules from multidimensional data, and (ii) the second one defines new operators similar in spirit to online analytical processing (OLAP) techniques to allow “data mining on demand” (i.e., data mining according to user’s needs and perspectives). The implementation of OLAP-like techniques relies on three operations on lattices, namely selection, projection and assembly. A detailed running example serves to illustrate the scope and benefits of the proposed techniques.