Financial Report Text Data Mining and Financial Fraud Based on Fusion of Kernel Density Clustering and Kernel Method

  • Lin Li, Yunxia Yao, Xu Zhou

Abstract

The number of listed companies in China is huge, and meanwhile is in the process of marketization. Therefore, the authenticity check of financial report text information still faces long-term arduous challenges. This thesis has studied the kernel density clustering and kernel method model, and constructed the model, data analysis and financial fraud information mining for the adaptability of the financial report text data mining in China’s listed enterprises. The research results show that the kernel density clustering and kernel method have higher accuracy, more intuitive results and convenient discriminating operations when constructing the financial report text data mining model. By using the kernel density clustering method in data mining, the overall kernel density clustering effect of listed enterprises is good, and the differences between categories are obvious, which can meet the practical application requirements.

Published
2020-09-24
How to Cite
Lin Li, Yunxia Yao, Xu Zhou. (2020). Financial Report Text Data Mining and Financial Fraud Based on Fusion of Kernel Density Clustering and Kernel Method. Design Engineering, 247 - 258. https://doi.org/10.17762/de.vi.148
Section
Articles