Title: Eliminating duplicate values of enterprise financial big data based on dynamic grid generation technology

Authors: Xiaoyang Li

Addresses: Department of Information Engineering, Xuancheng Vocational and Technical College, Xuancheng, 242000, China

Abstract: To improve the spatial reduction rate of the processed dataset and adjust the rand coefficient, this paper designs a method for removing duplicate values in enterprise financial big databased on dynamic grid generation technology. Firstly, denoising of enterprise financial big data is implemented through fast orthogonal wavelet transform. Secondly, based on dynamic grid generation technology, the fusion correlation features of enterprise financial data are constructed, and the correlation degree between data is calculated. Finally, use similarity clustering algorithms to cluster data with high correlation. For highly similar data in the same cluster, retain one record and exclude other identical data entries. The experimental results show that after applying this method, the spatial reduction rate of the dataset ranges from 9.61% to 15.55%, and the highest adjusted rand coefficient of the dataset can reach 0.997, indicating that this method effectively achieves the design expectations.

Keywords: enterprise financial data; data duplicate value; elimination process; dynamic grid generation technology; fusion of associated features; similar clustering algorithm.

DOI: 10.1504/IJITM.2026.152451

International Journal of Information Technology and Management, 2026 Vol.25 No.1, pp.61 - 72

Received: 09 Aug 2024
Accepted: 07 Feb 2025
Published online: 20 Mar 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article