Performance Evaluation, 7, 87-109.
Toward Integrating Data Warehousing with Data Mining Techniques 2
Copyright ?© 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission
of Idea Group Inc. is prohibited.
Chapter.XI
Toward.Integrating.
Data.Warehousing.
with.Data.Mining.
Techniques
Rokia Missaoui
Universit?© du Qu?©bec en Outaouais, Canada
Gana?«l Jatteau
Universit?© du Qu?©bec en Outaouais, Canada
Ameur Boujenoui
University of Ottawa, Canada
Sami Naouali
Universit?© du Qu?©bec en Outaouais, Canada
Abstract
In this chapter, 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: (1) the first one exploits concept lattices
for generating frequent closed itemsets, clusters and association rules from multidimensional
data, and (2) the second one defines new operators similar in spirit to
2 4 Missaoui, Jatteau, Boujenoui, & Naouali
Copyright ?© 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of
Idea Group Inc. is prohibited.
online analytical processing (OLAP) techniques to allow ???data mining on demand???
(i.
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