Result Comparison of Two Rough Set Based Discretization Algorithms

The area of knowledge discovery and data mining is growing rapidly. A large number of methods are employed to mine knowledge. Many of the methods rely of discrete data. However, most of the datasets used in real application have attributes with continuous values. To make the data mining techniques useful for such datasets, discretization is performed as a preprocessing step of the data mining. In this paper, we discuss rough set based discretization. We use UCI data sets to do experiments to compare the quality of Local discretization and Global discretization based on rough set. Our experiments show that Global discretization and Local discretization are dataset sensitive. Neither of them is always better than the other, though in some cases Global discretization generates far better results than Local discretization.