An associative classifier using weighted association rule

New classification approach that use association rule mining and classification has become a significant tool for knowledge discovery. An important advantage of these classification systems is that, using Association Rule Mining (ARM) they are able to examine several features at a time. While other state of art methods like decision tree or naive bayes classifiers consider that feature is independent of each other. Many applications can benefit from good classification model. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. There are many domains such as medical, where the maximum accuracy of the model is desired and hence the accuracy of the associative classifiers. In this paper, we propose a new framework (associative classifier) that uses weighted association rule mining (WARM). In any prediction model all attributes do not have same importance in predicting the class label. So different weights can be assigned to different attributes according to their predicting capability. We propose a theoretical model to introduce new associative classifier that takes advantage of weighted association rule mining. The model can be applied in any domain to improve the prediction accuracy.