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

DOI

10.2991/jracr.2012.2.2.6How to use a DOI?

Keywords

enterprise credit rating; Projection Pursuit; kernel density estimation; initial cluster centers; K-means clustering algorithm

Abstract

This paper proposes a new credit rating model for enterprises based on the 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.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Mu Zhang
AU  - Zongfang Zhou
PY  - 2012
DA  - 2012/08/01
TI  - A Credit Rating Model for Enterprises Based on Projection Pursuit and K-Means Clustering Algorithm
JO  - Journal of Risk Analysis and Crisis Response
SP  - 131
EP  - 138
VL  - 2
IS  - 2
SN  - 2210-8505
UR  - https://doi.org/10.2991/jracr.2012.2.2.6
DO  - 10.2991/jracr.2012.2.2.6
ID  - Zhang2012
ER  -