Due to the information age and the rapid development of Internet, the amount of data grows rapidly. Thus, the research topic nowadays focuses on how to capture valuable information among a large amount of data efficiently. The majority of machine learning algorithms can be applied only to data described by discrete numerical or nominal attributes, but continuous attribute data is the most common form of data. Discretization algorithm can divide a continuous attribute’s values into a finite number of intervals and simplify the complexity of data. It not only makes us easier to understand the distribution and characteristic of data, but ends the restriction of machine learning algorithms. In this paper, we propose CAGI(Class-Attribute Gini Index) discretization algorithm and compare with CAIM(Class-Attribute Interdependence Maximization) discretization algorithm and CACC(Class-Attribute Contingency Coefficient) discretization algorithm. The result of experiment shows that in some dataset, CAGI discretization algorithm not only discretizes the data more correctly, but improves the accuracy of classification.