Financial data mining and information disclosure supported by fuzzy logic algorithms

• Published in: Journal of computational methods in sciences and engineering Vol. 25; no. 1; pp. 591 - 604
• Main Authors: Liu, Yonghui, Wang, Zhenhua
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2025; Sage Publications Ltd
• Subjects: Data mining; Fuzzy logic; Genetic algorithms; adaptive neural networks; fuzzy logic algorithms; key features; information disclosure; financial data mining
• ISSN: 1472-7978; 1875-8983
• DOI: 10.1177/14727978251321954

Traditional financial data mining and information disclosure research faces problems such as huge data volume, uneven data quality, and untimely information disclosure. This paper aims to use fuzzy logic algorithms to process financial data, improve data mining efficiency, explore new information disclosure methods, and design an information disclosure evaluation model based on fuzzy logic algorithms. The experiment constructed a financial data mining model based on an adaptive neural fuzzy inference system (ANFIS), optimized the model parameters using a fuzzy genetic algorithm (FGA), and used a fuzzy comprehensive evaluation model to form information disclosure content with the help of logical reasoning. The research results show that the model accuracy of financial data mining reaches 96.38%, which has a good effect on information disclosure, and the algorithm efficiency is better than that of traditional algorithms.