Implementation of the Associative Classification Algorithm and Format of Dataset in Context of Data Mining

Abstract

Construction of classification models based on association rules. Although association rules have been predominantly used for data exploration and description, the interest in using them for prediction has rapidly increased in the data mining community. In order to mine only rules that can be used for classification, I had modified the well known association rule mining algorithm Apriori to handle user-defined input constraints. We considered constraints that require the presence/absence of particular items or that limit the number of items in the antecedents and/or the consequents of the rules. We developed a characterization of those item sets that will potentially form rules that satisfy the given constraints. This characterization allows us to prune during item set construction. This improves the time performance of item set construction. Using this characterization, we implemented a classification system based on association rules. Furthermore, I enhanced the algorithm by relaying on the typical support/confidence framework, and mining for the best possible rules above a user-defined minimum confidence and within a desired range for the number of rules[9]. This avoids long mining times that might produce large collections of rules with low predictive power.