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Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining

Received: 26 April 2018     Published: 27 April 2018
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Abstract

Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data mining to analyze talent information of teachers in Zhejiang University of Finance and Economics, China from 2011 to 2017, and then to predict their capabilities in obtaining National Foundation of China. In a word, this paper aims to provide decision support for universities’ talent introduction strategies. After data cleaning and feature engineering, Apriori algorithm is applied to mine the association rules and find key factors that are closely related to teachers' acquisition of National Science Foundation of China. Then we make predictions with four kinds of models, including Logistic Regression Model, Decision Tree Model, Artificial Neural Network Model and Support Vector Machine Model. In the end, in order to get a more accurate model, Logistic Regression Model which has the highest accuracy of prediction is used to do stepwise regression.

Published in International Journal of Statistical Distributions and Applications (Volume 4, Issue 1)
DOI 10.11648/j.ijsd.20180401.13
Page(s) 22-28
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), 2018. Published by Science Publishing Group

Keywords

Talent Introduction Strategies, Apriori Algorithm, Prediction Model, R Language

References
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[5] D. P. Zhang, and D. Jin, “The data mining of the human resources data warehouse in university based on association rule,” J. Comp., 2011, vol. 6, pp. 139-146.
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  • APA Style

    Xiao Yang, Caiyun Ying, Yefeng Zhou. (2018). Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining. International Journal of Statistical Distributions and Applications, 4(1), 22-28. https://doi.org/10.11648/j.ijsd.20180401.13

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    ACS Style

    Xiao Yang; Caiyun Ying; Yefeng Zhou. Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining. Int. J. Stat. Distrib. Appl. 2018, 4(1), 22-28. doi: 10.11648/j.ijsd.20180401.13

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    AMA Style

    Xiao Yang, Caiyun Ying, Yefeng Zhou. Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining. Int J Stat Distrib Appl. 2018;4(1):22-28. doi: 10.11648/j.ijsd.20180401.13

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  • @article{10.11648/j.ijsd.20180401.13,
      author = {Xiao Yang and Caiyun Ying and Yefeng Zhou},
      title = {Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining},
      journal = {International Journal of Statistical Distributions and Applications},
      volume = {4},
      number = {1},
      pages = {22-28},
      doi = {10.11648/j.ijsd.20180401.13},
      url = {https://doi.org/10.11648/j.ijsd.20180401.13},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsd.20180401.13},
      abstract = {Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data mining to analyze talent information of teachers in Zhejiang University of Finance and Economics, China from 2011 to 2017, and then to predict their capabilities in obtaining National Foundation of China. In a word, this paper aims to provide decision support for universities’ talent introduction strategies. After data cleaning and feature engineering, Apriori algorithm is applied to mine the association rules and find key factors that are closely related to teachers' acquisition of National Science Foundation of China. Then we make predictions with four kinds of models, including Logistic Regression Model, Decision Tree Model, Artificial Neural Network Model and Support Vector Machine Model. In the end, in order to get a more accurate model, Logistic Regression Model which has the highest accuracy of prediction is used to do stepwise regression.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining
    AU  - Xiao Yang
    AU  - Caiyun Ying
    AU  - Yefeng Zhou
    Y1  - 2018/04/27
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ijsd.20180401.13
    DO  - 10.11648/j.ijsd.20180401.13
    T2  - International Journal of Statistical Distributions and Applications
    JF  - International Journal of Statistical Distributions and Applications
    JO  - International Journal of Statistical Distributions and Applications
    SP  - 22
    EP  - 28
    PB  - Science Publishing Group
    SN  - 2472-3509
    UR  - https://doi.org/10.11648/j.ijsd.20180401.13
    AB  - Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data mining to analyze talent information of teachers in Zhejiang University of Finance and Economics, China from 2011 to 2017, and then to predict their capabilities in obtaining National Foundation of China. In a word, this paper aims to provide decision support for universities’ talent introduction strategies. After data cleaning and feature engineering, Apriori algorithm is applied to mine the association rules and find key factors that are closely related to teachers' acquisition of National Science Foundation of China. Then we make predictions with four kinds of models, including Logistic Regression Model, Decision Tree Model, Artificial Neural Network Model and Support Vector Machine Model. In the end, in order to get a more accurate model, Logistic Regression Model which has the highest accuracy of prediction is used to do stepwise regression.
    VL  - 4
    IS  - 1
    ER  - 

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

  • School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China

  • School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China

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