Affiliations: [a] School of Management Science and Engineering, Central University of Finance and Economics, Beijing, China
| [b] School of Statistics and Mathematics, Central University of Finance and Economics, Beijing, China
| [c] School of Finance, Central University of Finance and Economics, Beijing, China
Correspondence:
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Corresponding author. Lu Han, School of Management Science and Engineering, Central University of Finance and Economics, Beijing, China. E-mail: hanluivy@126.com.
Abstract: Ever increasing ordinal variables are being collected by the Personal Credit Reference System in China, however this system suffers from analysis of this kind of data, which cannot be calculated by Euclidean distance. In this study, we put forward a hybrid KNN algorithm based on Sugeno measure, and we prove that the error of this algorithm is smaller than that of Euclidean distance, furthermore, we use real data obtained from the Personal Credit Reference System to perform experiments and get the user’s initial portrait. Through the comparisons with Kmeans algorithm and other different distance measures in KNN algorithm, we find that the hybrid KNN algorithm is more suitable for clustering personal credit data.
