Input data for decision trees

Data Mining has been successful in a wide variety of application areas for varied purposes. Data Mining itself is done using several different methods. Decision Trees are one of the popular methods that have been used for Data Mining purposes. Since the process of constructing these decision trees assume no distributional patterns in the data (non-parametric), characteristics of the input data are usually not given much attention. We consider some characteristics of input data and their effect on the learning performance of decision trees. Preliminary results indicate that the performance of decision trees can be improved with minor modifications of input data.