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Influence of data mining technology in information analysis of human resource management on macroscopic economic management

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  • Ai Zhang

Abstract

The purposes are to manage human resource data better and explore the association between Human Resource Management (HRM), data mining, and economic management. An Ensemble Classifier-Decision Tree (EC-DT) algorithm is proposed based on the single decision tree algorithm to analyze HRM data. The involved single decision tree algorithms include C4.5, Random Tree, J48, and SimpleCart. Then, an HRM system is established based on the designed algorithm, and the evaluation management and talent recommendation modules are tested. Finally, the designed algorithm is compared and tested. Experimental results suggest that C4.5 provides the highest classification accuracy among the single decision tree algorithms, reaching 76.69%; in contrast, the designed EC-DT algorithm can provide a classification accuracy of 79.97%. The proposed EC-DT algorithm is compared with the Content-based Recommendation Method (CRM) and the Collaborative Filtering Recommendation Method (CFRM), revealing that its Data Mining Recommendation Method (DMRM) can provide the highest accuracy and recall, reaching 35.2% and 41.6%, respectively. Therefore, the data mining-based HRM system can promote and guide enterprises to develop according to quantitative evaluation results. The above results can provide a reference for studying HRM systems based on data mining technology.

Suggested Citation

  • Ai Zhang, 2021. "Influence of data mining technology in information analysis of human resource management on macroscopic economic management," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-12, May.
    • Handle: RePEc:plo:pone00:0251483
    DOI: 10.1371/journal.pone.0251483
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    References listed on IDEAS

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    1. Weiwei Huo & Xianmiao Li & Manyi Zheng & Yan Liu & Jinyi Yan, 2020. "Commitment to Human Resource Management of the Top Management Team for Green Creativity," Sustainability, MDPI, vol. 12(3), pages 1-16, January.
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    3. Gokhan Ozkaya & Mehpare Timor & Ceren Erdin, 2021. "Science, Technology and Innovation Policy Indicators and Comparisons of Countries through a Hybrid Model of Data Mining and MCDM Methods," Sustainability, MDPI, vol. 13(2), pages 1-49, January.
    4. Soyoung Park & Se-Yeong Hamm & Jinsoo Kim, 2019. "Performance Evaluation of the GIS-Based Data-Mining Techniques Decision Tree, Random Forest, and Rotation Forest for Landslide Susceptibility Modeling," Sustainability, MDPI, vol. 11(20), pages 1-20, October.
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