Digital Industry Financial Risk Early Warning System Based on Improved K-Means Clustering Algorithm

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 r...

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Bibliographic Details
Published in:Computational intelligence and neuroscience Vol. 2022; pp. 1 - 9
Main Authors: Duan, Xiao-li, Du, Xue-xia, Guo, Li-mei
Format: Journal Article
Language:English
Published: United States Hindawi 28.05.2022
John Wiley & Sons, Inc
Subjects:
Algorithms
Annual reports
Artificial intelligence
Big Data
Cluster Analysis
Clustering
Computational linguistics
Data collection
Datasets
Early warning systems
Financial risk
International finance
Language processing
Natural language interfaces
Natural language processing
Noise
Vector quantization
China
ISSN:1687-5265, 1687-5273, 1687-5273
Online Access:Get full text
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Summary: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.
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Academic Editor: Qiangyi Li
ISSN:1687-5265
1687-5273
1687-5273
DOI:10.1155/2022/6797185