Capturing uncertainty in associative classification model

This paper aims to propose a weighted linguistic associative classification model for uncertainty data analysis using rough membership function. Transformation of quantitative association rules into linguistic representation can be achieved in discretizing the numerical interval into rough interval described with respective rough membership values. Transformation of linguistic information system is suggested prior to the frequent pattern discovery. Neither pruning of association rules nor classifier modelling is needed. The rough membership values of the each linguistic frequent item are composited to form the weighted associative classification rule. Simulated results on iris plant dataset were shown in the paper. The future work of the research will focus on implementing the model with more experimental dataset.