Fine Clustering Analysis of Internet Financial Credit Investigation Based on Big Data

Given that the traditional methods cannot perform clustering analysis on the Internet financial credit reporting directly and effectively, precise clustering analysis of internet financial credit reporting dependent on multidimensional attribute sparse large data is proposed. By measuring the overall distance between Internet financial credit reporting through the sparse extensive data with multidimensional attributes, the multidimensional attribute sparse large data are used to perform clustering analysis on the overall distance matrix and the component approximate distance matrix between the data, respectively. Numerical experiments show that the method (MASLD) proposed in this paper can reflect the overall data features effectively but also improve the clustering effect of the original Internet financial credit reporting data through the analysis of the correlation relationship between the vital components attribute sequences. The (MASLD) method proposed in this paper could be applied to medical, voice, text, fault monitoring, and other Internet financial credit reporting pattern recognition and knowledge discovery properly. (C) 2021 Published by Elsevier Inc.