A Credit Rating Model for Enterprises Based on Projection Pursuit and K-Means Clustering Algorithm

Abstract

This paper proposes a new credit rating model for enterprises based on Projection Pursuit and K-means clustering algorithm. Firstly, using Projection Pursuit, the comprehensive credit score of each sample is obtained, so as to reflect the structure or characteristics of original multi-dimensional data. Secondly, the distribution density of the comprehensive credit score series is estimated by the kernel density estimation method, and then the initial cluster centers in original high dimension space are determined according to the local maximum points of density function. Finally, starting from the initial cluster centers above, using K-means clustering algorithm, the final cluster centers are obtained, and then the credit grades are partitioned. Thus, the credit rating for enterprises is realized. Taking the high-tech listed companies in China as samples, it is proved that the model proposed by this paper is feasible and effective.