The early warning of financial risk is to identify and analyze existing financial risk factors, determine the possibility and severity of occurring risks, and provide scientific basis for risk prevention and management. The fragility of financial system and the destructiveness of financial crisis make it extremely important to build a good financial risk early-warning mechanism. The main idea of the K-means clustering algorithm is to gradually optimize clustering results and constantly redistribute target dataset to each clustering center to obtain optimal solution; its biggest advantage lies in its simplicity, speed, and objectivity, being widely used in many research fields such as data processing, image recognition, market analysis, and risk evaluation. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of financial risk early-warning, elaborated the development background, current status and future challenges of the K-means clustering algorithm, introduced the related works of similarity measure and item clustering, proposed a financial risk indicator system based on the K-means clustering algorithm, performed indicator selection and data processing, constructed a financial risk early-warning model based on the K-means clustering algorithm, conducted the classification of financial risk types and optimization of financial risk control, and finally carried out an empirical experiments and its result analysis. The study results show that the K-means clustering method can effectively avoid the subjective negative impact caused by artificial division thresholds, continuously optimize the prediction process of financial risk and redistribute target dataset to each cluster center for obtaining optimized solution, so the algorithm can more accurately and objectively distinguish the state interval of different financial risks, determine risk occurrence possibility and its severity, and provide a scientific basis for risk prevention and management. The study results of this paper provide a reference for further researches on financial risk early-warning based on K-means clustering algorithm.

中文翻译：

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基于K均值聚类算法的金融风险预警。

金融风险预警是识别和分析现有金融风险因素，确定发生风险的可能性和严重性，为风险的预防和管理提供科学依据。金融体系的脆弱性和金融危机的破坏性使得建立良好的金融风险预警机制显得尤为重要。K-means聚类算法的主要思想是逐步优化聚类结果，并不断地将目标数据集重新分配给每个聚类中心以获得最佳解决方案。它的最大优势在于它的简单性，速度和客观性，被广泛用于许多研究领域，例如数据处理，图像识别，市场分析和风险评估。在总结和分析以前的研究工作的基础上，阐述了金融风险预警的研究现状和意义，阐述了K-means聚类算法的发展背景，现状和未来挑战，介绍了相似度度量和项目聚类的相关工作，提出了金融风险指标基于K-means聚类算法的系统，进行指标选择和数据处理，构建基于K-means聚类算法的金融风险预警模型，进行金融风险类型分类和金融风险控制优化，最后进行了实证实验及其结果分析。研究结果表明，K-means聚类方法可以有效避免人为划分阈值引起的主观负面影响，不断优化财务风险的预测过程，并将目标数据集重新分配到每个聚类中心以获得优化的解决方案，从而使该算法可以更准确，客观地区分不同财务风险的状态区间，确定风险发生的可能性及其严重程度，并提供科学的依据。风险预防和管理的基础。本文的研究结果为进一步研究基于K-means聚类算法的金融风险预警提供参考。为预防和管理风险提供科学依据。本文的研究结果为进一步研究基于K-means聚类算法的金融风险预警提供参考。为预防和管理风险提供科学依据。本文的研究结果为进一步研究基于K-means聚类算法的金融风险预警提供参考。