The Application of Internal Control Defect Identification under Data Mining Technology

In order to meet the challenges of application research in internal control defect identification, in view of the shortcomings of the existing fuzzy recognition algorithms, this study introduces an innovative application research method in defect identification based on data mining technology. This new approach uses the principles of data set theory to accurately identify and locate key influencing factors, and accordingly makes a sensible classification of indicators to reduce potential interference. At the same time, using the unique mechanism of data mining technology, this scheme cleverly constructs the design strategy of identification. The empirical results show that the proposed scheme shows a significant improvement compared with the traditional fuzzy recognition algorithm in terms of the accuracy of the application research and the processing efficiency of key factors in defect recognition, showing its obvious strong advantages. In internal control, the applied research in defect identification plays a crucial role, which can accurately predict and optimize the growth trend and output results of the applied research in the identification of internal control defects. However, in the face of complex simulation tasks, traditional fuzzy recognition algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of application research in defect identification of data mining technology optimization, accurately controls the influencing parameters through the data set theory, and uses this as the road map for index allocation, and then uses data mining technology innovation to construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the application research of internal control defect identification, the simulation scheme based on data mining technology successfully overcomes the shortcomings of the traditional fuzzy recognition algorithm, and significantly improves the accuracy and operation efficiency of the simulation.