Abstract
Associative classification (AC) is an approach in data mining that uses association rule to build classification systems that are easy to interpret by end-user. When different data operations (adding, deleting, updating) are applied against certain training data set, the majority of current AC algorithms must scan the complete training data set again to update the results (classifier) in order to reflect change caused by such operations. This paper deals with data insertion issue within the incremental learning in AC mining. Particularly, we modified a known AC algorithm called CBA to treat one aspect of the incremental learning problem which is data insertion. Experimental results against six data sets from UCI data repository showed that the proposed incremental algorithm reduces the computational time if compared to CBA, and almost derives the same accuracy of it.