School of Economics &Management, Zhengzhou Normal University, Zhengzhou 450044, China.
National Central City Academy, Zhengzhou Normal University, Zhengzhou 450044, China.
Comput Intell Neurosci. 2022 May 28;2022:6797185. doi: 10.1155/2022/6797185. eCollection 2022.
Corporate financial risks not only endanger the financial stability of digital industry but also cause huge losses to the macro-economy and social wealth. In order to detect and warn digital industry financial risks in time, this paper proposes an early warning system of digital industry financial risks based on improved K-means clustering algorithm. Aiming to speed up the K-means calculation and find the optimal clustering subspace, a specific transformation matrix is used to project the data. The feature space is divided into clustering space and noise space. The former contains all spatial structure information; the latter does not contain any information. Each iteration of K-means is carried out in the clustering space, and the effect of dimensionality screening is achieved in the iteration process. At the same time, the retained dimensions are fed back to the next iteration. The dimensional information of the cluster space is discovered automatically, so no additional parameters are introduced. Experimental results show that the accuracy of the proposed algorithm is higher than other algorithms in financial risk detection.
企业财务风险不仅危及数字产业的金融稳定，也会给宏观经济和社会财富造成巨大损失。为了及时检测和预警数字产业财务风险，本文提出了一种基于改进 K-means 聚类算法的数字产业财务风险预警系统。为了加快 K-means 的计算速度并找到最优聚类子空间，使用特定的变换矩阵来对数据进行投影。特征空间被划分为聚类空间和噪声空间。前者包含所有空间结构信息；后者不包含任何信息。在聚类空间中执行 K-means 的每次迭代，并在迭代过程中实现维度筛选的效果。同时，将保留的维度反馈到下一次迭代中。自动发现聚类空间的维度信息，因此不会引入额外的参数。实验结果表明，该算法在财务风险检测中的准确性高于其他算法。
