Title: Rule Pruning
in Associative Classification Mining
Abstract: Classification
and association rule discovery are important data mining tasks. Using association
rule discovery to construct classification systems, also known as associative
classification, is a promising approach. In this paper, we survey different
rule pruning methods used by associative classification techniques. Furthermore,
we compare the effect of three pruning methods (database coverage, pessimistic
error estimation, lazy pruning) on the number of rules derived from different
classification data sets. Results obtained from experimenting on fourteen
data sets from the UCI data collection show the need for additional constraints
during pruning in associative classification in order to decrease further
the size of the resulting classifiers. The results also indicate that lazy
pruning algorithms generate very large number of rules if compared with other
associative algorithms. This may reduce their use for data mining applications
where a concise set of rules are required