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Enterprise Operating State Evaluation Based on Association Rule Algorithm and Data Set.

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  • 1School of Management, Wuzhou University, Wuzhou, Guangxi 543002, China.

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This study introduces an association rule algorithm for evaluating enterprise operating efficiency, analyzing financial data from listed companies. Findings reveal trends in asset turnover and operational status across multiple enterprises.

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Area of Science:

  • Business and Management
  • Data Science
  • Financial Analysis

Background:

  • Evaluating enterprise operating efficiency is crucial for objective business assessments.
  • Existing methods may have limitations in accuracy and objectivity.
  • Listed companies provide a robust dataset for operational analysis.

Purpose of the Study:

  • To enhance the quality and objectivity of enterprise operating efficiency evaluations.
  • To reduce errors and invalid data in partition analysis.
  • To propose a novel evaluation method using association rules and datasets.

Main Methods:

  • Utilizing an association rule algorithm and dataset for efficiency evaluation.
  • Analyzing operating efficiency from horizontal and vertical dimensions.
  • Employing Kendall's tau_b for correlation testing on total asset operating costs.

Main Results:

  • Operating efficiency was scientifically analyzed using financial indicators.
  • Longitudinal comparison showed 63.16% of enterprises had year-on-year increases in efficiency.
  • A significant portion of enterprises (6 out of 19) exhibited a declining operational trend.

Conclusions:

  • The proposed method offers a more objective evaluation of enterprise operating conditions.
  • The analysis highlights varying operational trends among listed companies.
  • The study provides insights into enterprise operating status and scale through data-driven analysis.