This article comments on data mining and rough set theory, regarding the article "Myths About Rough Set Theory," published in the November 1998 issue of the journal "Communications of the ACM." The authors of this article raise some important issues and express some legitimate concerns. The authors express that rough data set theory is not the only discipline in which discretization is necessary or deals with complex data, cited as two problems. The third problem raised by the authors is associated with the difference between objective and subjective approaches to uncertainty. The article explains each problem with examples of grading student work, a table of 10 attributes with 20 values each to illustrate complexities involved in data analysis. As per the article, discretization is a technique used in many areas, including machine learning and learning in networks, and is definitely not restricted. Finally, regarding the authors' comments about objectivity of rough set theory, the author related Dempster-Shafer theory and the rough set theory, citing the definition of probability, as an example. The author concluded that input data must be given to initiate rough set theory procedures, and when rough set theory comes into the picture, its methods are objective with respect to given data.