01850nas a2200193   4500008004100000245007600041210006900117260000900186300001400195490000600209520120400215653002101419653003101440653001901471653001601490653001701506100001601523856011701539       2006                        eng d00aPruning techniques in associative classification: Survey and comparison0 aPruning techniques in associative classification Survey and comp  c2006  a197 - 2020 v43 aAssociation 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.10aAssociation rule10aAssociative classification10aClassification10aData mining10aRule pruning1 aThabtah, F.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33749400880&partnerID=40&md5=0633368b3744eefc60e6e205c64d95f4