Review of: "Application of Data Mining Combined with K-means Clustering Algorithm in Enterprises' Risk Audit"

Résumé

Potential competing interests: No potential competing interests to declare.The manuscript investigates the potential of applying data mining techniques, particularly K-means clustering, to improve enterprise risk audit processes.The authors propose a methodology that integrates data mining with traditional risk audit practices.This approach aims to analyze large volumes of enterprise data (financial records, transactions, etc.) to identify patterns and anomalies that might indicate risk.By clustering similar activities or entities, auditors can prioritize their efforts and focus on areas with higher risk profiles. Strengths:The paper explores the application of data mining techniques in enterprise risk audit, a potentially valuable area of research.The authors propose a methodology that integrates K-means clustering with traditional risk audit practices.The paper highlights the potential benefits of data mining for automating and enhancing risk identification and assessment.Weaknesses: Novelty and Contribution: Several reviewers noted a lack of clear novelty in the research.The paper needs to emphasize how this work builds upon or differs from existing studies and what specific contributions it makes to the field. Technical Clarity and Methodology:The methodology section requires improvement.Reviewers pointed out the need for a clearer explanation of the data mining techniques used, including preprocessing procedures, dataset characteristics, and the integration of K-means and RF-LightGBM. Results and Discussion:The paper lacks a deep analysis of the obtained results.A more comprehensive discussion of model performance metrics and the factors contributing to its success is needed. Writing and Organization:The overall writing and organization of the paper could be improved.Reviewers suggested restructuring sections (e.g., separating theoretical background from case study) and addressing formatting inconsistencies.

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