1. A method for generating an interpretable decision tree for data sets that do not have supervised information, the method comprising of the steps of: (a) determining a measure of importance of a selected attribute of a data set; (b) classifying data of the data set into a number of subsets based upon the selected attribute; and (c) applying a stopping criteria for splitting a node based on a measure of homogeneity or inhomogeneity in the data set.