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Application of Association Rule Mining in Talent Introduction Analysis

Received: 5 August 2019     Published: 27 September 2019
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Abstract

With the advancement of higher education, many colleges have given increasing attention to talent introduction. On the other hand, the association rule mining technique is a useful method which extracts the useful association rules from the complex data repositories. This study takes the example of 245 academic staff from Zhejiang University of Finance and Economics, China and uses Apriori algorithm to explore the association rules on whether an academic staff can obtain the Natural Science Foundation of China (NSFC) within three years after s/he is recruited to the university. The aim of this study is to better introduce talents for colleges so that the academic levels of colleges can be improved. The results of association rule mining have shown that having published high quality papers such as SCI paper and SSCI paper has an important effect on the probability of academic staff to obtain NSFC within three years. Besides, the grade of PhD school has also an effect on the probability of academic staff to obtain NSFC within three years. The higher the grade of a staff’s PhD school is, the easier for him to obtain NSFC within three years.

Published in Science Journal of Applied Mathematics and Statistics (Volume 7, Issue 3)
DOI 10.11648/j.sjams.20190703.13
Page(s) 45-50
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Data Mining, Association Rule Mining, Apriori Algorithm

References
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Cite This Article
  • APA Style

    Zixuan Chen, Jiepin Ding, Zhiguang Zhou, Yin Zhu, Wenyu Zhang. (2019). Application of Association Rule Mining in Talent Introduction Analysis. Science Journal of Applied Mathematics and Statistics, 7(3), 45-50. https://doi.org/10.11648/j.sjams.20190703.13

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    ACS Style

    Zixuan Chen; Jiepin Ding; Zhiguang Zhou; Yin Zhu; Wenyu Zhang. Application of Association Rule Mining in Talent Introduction Analysis. Sci. J. Appl. Math. Stat. 2019, 7(3), 45-50. doi: 10.11648/j.sjams.20190703.13

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    AMA Style

    Zixuan Chen, Jiepin Ding, Zhiguang Zhou, Yin Zhu, Wenyu Zhang. Application of Association Rule Mining in Talent Introduction Analysis. Sci J Appl Math Stat. 2019;7(3):45-50. doi: 10.11648/j.sjams.20190703.13

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  • @article{10.11648/j.sjams.20190703.13,
      author = {Zixuan Chen and Jiepin Ding and Zhiguang Zhou and Yin Zhu and Wenyu Zhang},
      title = {Application of Association Rule Mining in Talent Introduction Analysis},
      journal = {Science Journal of Applied Mathematics and Statistics},
      volume = {7},
      number = {3},
      pages = {45-50},
      doi = {10.11648/j.sjams.20190703.13},
      url = {https://doi.org/10.11648/j.sjams.20190703.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20190703.13},
      abstract = {With the advancement of higher education, many colleges have given increasing attention to talent introduction. On the other hand, the association rule mining technique is a useful method which extracts the useful association rules from the complex data repositories. This study takes the example of 245 academic staff from Zhejiang University of Finance and Economics, China and uses Apriori algorithm to explore the association rules on whether an academic staff can obtain the Natural Science Foundation of China (NSFC) within three years after s/he is recruited to the university. The aim of this study is to better introduce talents for colleges so that the academic levels of colleges can be improved. The results of association rule mining have shown that having published high quality papers such as SCI paper and SSCI paper has an important effect on the probability of academic staff to obtain NSFC within three years. Besides, the grade of PhD school has also an effect on the probability of academic staff to obtain NSFC within three years. The higher the grade of a staff’s PhD school is, the easier for him to obtain NSFC within three years.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - Application of Association Rule Mining in Talent Introduction Analysis
    AU  - Zixuan Chen
    AU  - Jiepin Ding
    AU  - Zhiguang Zhou
    AU  - Yin Zhu
    AU  - Wenyu Zhang
    Y1  - 2019/09/27
    PY  - 2019
    N1  - https://doi.org/10.11648/j.sjams.20190703.13
    DO  - 10.11648/j.sjams.20190703.13
    T2  - Science Journal of Applied Mathematics and Statistics
    JF  - Science Journal of Applied Mathematics and Statistics
    JO  - Science Journal of Applied Mathematics and Statistics
    SP  - 45
    EP  - 50
    PB  - Science Publishing Group
    SN  - 2376-9513
    UR  - https://doi.org/10.11648/j.sjams.20190703.13
    AB  - With the advancement of higher education, many colleges have given increasing attention to talent introduction. On the other hand, the association rule mining technique is a useful method which extracts the useful association rules from the complex data repositories. This study takes the example of 245 academic staff from Zhejiang University of Finance and Economics, China and uses Apriori algorithm to explore the association rules on whether an academic staff can obtain the Natural Science Foundation of China (NSFC) within three years after s/he is recruited to the university. The aim of this study is to better introduce talents for colleges so that the academic levels of colleges can be improved. The results of association rule mining have shown that having published high quality papers such as SCI paper and SSCI paper has an important effect on the probability of academic staff to obtain NSFC within three years. Besides, the grade of PhD school has also an effect on the probability of academic staff to obtain NSFC within three years. The higher the grade of a staff’s PhD school is, the easier for him to obtain NSFC within three years.
    VL  - 7
    IS  - 3
    ER  - 

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Author Information
  • School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

  • School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

  • School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

  • School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

  • School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

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