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

Xiao-Li Duan1, Xue-Xia Du2, Li-Mei Guo3

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This study introduces an improved K-means clustering algorithm to detect digital industry financial risks early. The new system enhances accuracy in identifying corporate financial instability, safeguarding economic and social wealth.

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Area of Science:

  • Financial Risk Management
  • Data Mining
  • Computational Economics

Background:

  • Corporate financial risks pose significant threats to the digital industry's stability.
  • These risks can lead to substantial losses in the macro-economy and social wealth.
  • Timely detection and early warning systems are crucial for mitigating these impacts.

Purpose of the Study:

  • To propose an early warning system for digital industry financial risks.
  • To enhance the accuracy and efficiency of financial risk detection.
  • To address the challenges of identifying and mitigating corporate financial instability.

Main Methods:

  • An improved K-means clustering algorithm is developed for financial risk detection.
  • A transformation matrix is employed to project data, dividing feature space into clustering and noise subspaces.
  • The algorithm iteratively refines clustering within the identified clustering space, achieving dimensionality screening automatically.

Main Results:

  • The proposed algorithm demonstrates higher accuracy in financial risk detection compared to existing methods.
  • The system effectively identifies and warns about digital industry financial risks.
  • Dimensionality screening is achieved automatically without introducing additional parameters.

Conclusions:

  • The improved K-means clustering algorithm provides an effective solution for early warning of digital industry financial risks.
  • The system enhances the accuracy and efficiency of risk detection, contributing to financial stability.
  • Automatic discovery of cluster space dimensions improves the algorithm's performance and applicability.