Effect of Improved Association Algorithm on Mining and Recognition of Audit Data

Abstract

Audit evidence is the proof material on which the auditors issue audit opinions and make relevant audit conclusions, and audit evidence in the era of big data presents new characteristics in terms of adequacy, relevance, and reliability. This paper combines the improved association algorithm to construct the audit data mining system to improve the data processing effect of the audit process. Moreover, this paper proposes a new dynamic threshold method, gives the calculation method of some important parameters in the algorithm, and presents the improved cell-based association algorithm flow. In addition, this paper discusses how the outlier algorithm is applied to the acquisition of audit evidence and the application scenarios in the audit system. The experimental research results show that the audit data mining system based on the improved association algorithm proposed in this paper has a good effect in the audit of accounting and financial data.