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Influence of data mining technology in information analysis of human resource management on macroscopic economic management
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 inv...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130961/ https://www.ncbi.nlm.nih.gov/pubmed/34003830 http://dx.doi.org/10.1371/journal.pone.0251483 |
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author | Zhang, Ai |
author_facet | Zhang, Ai |
author_sort | Zhang, Ai |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8130961 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81309612021-05-27 Influence of data mining technology in information analysis of human resource management on macroscopic economic management Zhang, Ai PLoS One Research Article 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. Public Library of Science 2021-05-18 /pmc/articles/PMC8130961/ /pubmed/34003830 http://dx.doi.org/10.1371/journal.pone.0251483 Text en © 2021 Ai Zhang https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Zhang, Ai Influence of data mining technology in information analysis of human resource management on macroscopic economic management |
title | Influence of data mining technology in information analysis of human resource management on macroscopic economic management |
title_full | Influence of data mining technology in information analysis of human resource management on macroscopic economic management |
title_fullStr | Influence of data mining technology in information analysis of human resource management on macroscopic economic management |
title_full_unstemmed | Influence of data mining technology in information analysis of human resource management on macroscopic economic management |
title_short | Influence of data mining technology in information analysis of human resource management on macroscopic economic management |
title_sort | influence of data mining technology in information analysis of human resource management on macroscopic economic management |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8130961/ https://www.ncbi.nlm.nih.gov/pubmed/34003830 http://dx.doi.org/10.1371/journal.pone.0251483 |
work_keys_str_mv | AT zhangai influenceofdataminingtechnologyininformationanalysisofhumanresourcemanagementonmacroscopiceconomicmanagement |