Classification rule mining aims to discover a small set of
rules in the database that forms an accurate classifier.
Association rule mining finds all the rules existing in the
database that satisfy some minimum support and minimum
confidence constraints. For association rule mining, the
target of discovery is not pre-determined, while for
classification rule mining there is one and only one predetermined
target. In this paper, we propose to integrate
these two mining techniques. The integration is done by
focusing on mining a special subset of association rules,
called class association rules (CARs). An efficient
algorithm is also given for building a classifier based on the
set of discovered CARs. Experimental results show that the
classifier built this way is, in general, more accurate than
that produced by the state-of-the-art classification system
C4.5. In addition, this integration helps to solve a number
of problems that exist in the current classification systems.