A GLOBAL DISCRETIZATION METHOD BASED ON CLUSTERING AND ROUGH SET

Discretization is an important algorithm and considered to be a process of information generalization and data reduction. To avoid information loss and total number of cut point decrease after discretization of continuous attributes, based on multi-attribute discretization algorithm with good global clustering effects for selecting candidate cut points is proposed. The improved algorithm is combined with the advantages of clustering method and algorithm based on the importance of cut points, The experimental results show that the proposed algorithm can significantly decrease the number of discretization cut points and increase the predictive accuracy of the classifier than both.