This paper discusses the correlation between the number of cut points, granular entropy and classification accuracy in discretization. It is proven that granular entropy decreases if the number of cut points increases. A hybrid discretization algorithm is proposed to provide discretization schemes for studying these measures. The simulation experiments show that the absolute value of the correlation coefficient between number of cut points and classification accuracy is quite large, as it for granular entropy and classification accuracy. Sometimes, the correlation between the granular entropy and classification accuracy is smaller than that between the cut points and classification accuracy.