Discretization algorithms play an important role in many areas such as data mining, machine learning and artificial
intelligence. Therefore, a heuristic discretization technique based on the class-attribute interdependence is proposed. A new
discretization criterion is defined, which selects best cut points in terms of characteristics of the data itself and overcomes the
existing deficiencies of state-of-the-art top-down discretization methods. A novel measure of inconsistency based on variable
precision rough sets(VPRS) model is developed, which effectively controls information loss generated by discretization
and allows an adaptive degree of misclassification. Empirical experiments and statistical analysis show that the proposed
technique generates a better discretization scheme which significantly improves the accuracy of classification by running
J4.8 and SVM.