Recently, there
is an increasing interest for applying/adapting data mining techniques and advanced
statistical analysis (e.g., cluster analysis, principal component analysis, log-linear
modeling) for knowledge discovery (Ben Messaoud, Boussa??d, & Rabas?©da, 2004;
Lu, Feng, & Han, 2000; Sarawagi, Agrawal, & Megiddo, 1998) and data compression
or query approximation purposes in data cubes (Babcock, Chaudhuri, & Das,
2003; Barbara & Wu, 2001).
The objective of this chapter is to propose techniques for reinforcing the collaboration
and linkage between DW and DM techniques by using formal concept analysis
and concept lattices (Ganter & Wille, 1999) as a sound and theoretical framework
for data mining. More precisely, we first present our view of rule mining in data
cubes. Then, we adapt the interactive exploratory mechanisms inherent to online
analytical processing (OLAP) techniques to the framework of data mining tools and
techniques in order to help the user select the appropriate subset of an already existing
data mining output. To conduct the first task, we discuss association rule mining
in multidimensional data and show how cube clustering using concept lattices and
frequent closed itemsets can be exploited for generating meaningful association
Toward Integrating Data Warehousing with Data Mining Techniques 2
Copyright ?© 2007, Idea Group Inc.
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