Abstract: To solve the problem of credit rating of loan application,this paper introduces the basic concepts and preliminaries of clustering analysis employed in this study to handle the problem of estimation on the loan grade of clients,and presents two kinds of clustering algorithms for solving this problem.One clustering approach is based on credit data,called δ-kmeans,and the other approach can be applied to high dimensional credit data,called ASC.Extensive experiments were conducted to compare the performance between the proposed two algorithms.The experiments show that δ-kmeans algorithm achieves better results in credit risk control,but the ASC algorithm is more effective than traditional k-means algorithms and the Coweb algorithm in clustering high dimensional credit data.In addition,this paper analyzes the dynamics of cluster analysis on bank credit data in terms of the k-means algorithm.