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

Abstract

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