Pruning techniques in associative classification: Survey and comparison

Association rule discovery and classification are common data mining tasks. Integrating association rule and classification also known as associative classification is a promising approach that derives classifiers highly competitive with regards to accuracy to that of traditional classification approaches such as rule induction and decision trees. However, the size of the classifiers generated by associative classification is often large and therefore pruning becomes an essential task. In this paper, we survey different rule pruning methods used by current associative classification techniques. Further, we compare the effect of three pruning methods (database coverage, pessimistic error estimation, lazy pruning) on the accuracy rate and the number of rules derived from different classification data sets. Results obtained from experimenting on different data sets from UCI data collection indicate that lazy pruning algorithms may produce slightly higher predictive classifiers than those which utilise database coverage and pessimistic error pruning methods. However, the potential use of such classifiers is limited because they are difficult to understand and maintain by the end-user.