Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics

Wang Qin; Zhang Kangkang; Chen Huiting

doi:doi:10.11648/j.ajam.20180602.15

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Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics

Wang Qin, Zhang Kangkang, Chen Huiting

Published in American Journal of Applied Mathematics (Volume 6, Issue 2)

Received: 26 April 2018 Accepted: Published: 27 April 2018

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Abstract

In recent years, the issues of the talents introduction have attracted more and more researchers' and college administrators' attention. In the era of big data, data mining technology is widely used in various fields and has achieved remarkable results. The application of data mining technology in the introduction of university talents is in the ascendant. This paper uses the effective information of 245 teachers recruited by Zhejiang University of Finance & Economics since 2011 to explore and model the association rules. It preprocesses the raw information data by hierarchical clustering, and use Apriori algorithm to obtain a set of rules for the paper score and the situation of receiving the National Foundation of China (NFC) in 3 years. These rules will provide a constructive guiding significance for the introduction of talents in Zhejiang University of Finance & Economics.

Published in	American Journal of Applied Mathematics (Volume 6, Issue 2)
DOI	10.11648/j.ajam.20180602.15
Page(s)	55-61
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), 2024. Published by Science Publishing Group

Keywords

Association Rule Mining, Talents Introduction, Apriori Algorithm

References

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APA Style

Wang Qin, Zhang Kangkang, Chen Huiting. (2018). Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics. American Journal of Applied Mathematics, 6(2), 55-61. https://doi.org/10.11648/j.ajam.20180602.15

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ACS Style

Wang Qin; Zhang Kangkang; Chen Huiting. Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics. Am. J. Appl. Math. 2018, 6(2), 55-61. doi: 10.11648/j.ajam.20180602.15

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AMA Style

Wang Qin, Zhang Kangkang, Chen Huiting. Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics. Am J Appl Math. 2018;6(2):55-61. doi: 10.11648/j.ajam.20180602.15

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@article{10.11648/j.ajam.20180602.15,
  author = {Wang Qin and Zhang Kangkang and Chen Huiting},
  title = {Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics},
  journal = {American Journal of Applied Mathematics},
  volume = {6},
  number = {2},
  pages = {55-61},
  doi = {10.11648/j.ajam.20180602.15},
  url = {https://doi.org/10.11648/j.ajam.20180602.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20180602.15},
  abstract = {In recent years, the issues of the talents introduction have attracted more and more researchers' and college administrators' attention. In the era of big data, data mining technology is widely used in various fields and has achieved remarkable results. The application of data mining technology in the introduction of university talents is in the ascendant. This paper uses the effective information of 245 teachers recruited by Zhejiang University of Finance & Economics since 2011 to explore and model the association rules. It preprocesses the raw information data by hierarchical clustering, and use Apriori algorithm to obtain a set of rules for the paper score and the situation of receiving the National Foundation of China (NFC) in 3 years. These rules will provide a constructive guiding significance for the introduction of talents in Zhejiang University of Finance & Economics.},
 year = {2018}
}

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TY  - JOUR
T1  - Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics
AU  - Wang Qin
AU  - Zhang Kangkang
AU  - Chen Huiting
Y1  - 2018/04/27
PY  - 2018
N1  - https://doi.org/10.11648/j.ajam.20180602.15
DO  - 10.11648/j.ajam.20180602.15
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 55
EP  - 61
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20180602.15
AB  - In recent years, the issues of the talents introduction have attracted more and more researchers' and college administrators' attention. In the era of big data, data mining technology is widely used in various fields and has achieved remarkable results. The application of data mining technology in the introduction of university talents is in the ascendant. This paper uses the effective information of 245 teachers recruited by Zhejiang University of Finance & Economics since 2011 to explore and model the association rules. It preprocesses the raw information data by hierarchical clustering, and use Apriori algorithm to obtain a set of rules for the paper score and the situation of receiving the National Foundation of China (NFC) in 3 years. These rules will provide a constructive guiding significance for the introduction of talents in Zhejiang University of Finance & Economics.
VL  - 6
IS  - 2
ER  -

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Author Information

Wang Qin

School of Economics, Zhejiang University of Finance & Economics, Hangzhou, China
Zhang Kangkang

School of Economics, Zhejiang University of Finance & Economics, Hangzhou, China
Chen Huiting

School of Economics, Zhejiang University of Finance & Economics, Hangzhou, China

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APA Style

Wang Qin, Zhang Kangkang, Chen Huiting. (2018). Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics. American Journal of Applied Mathematics, 6(2), 55-61. https://doi.org/10.11648/j.ajam.20180602.15

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ACS Style

Wang Qin; Zhang Kangkang; Chen Huiting. Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics. Am. J. Appl. Math. 2018, 6(2), 55-61. doi: 10.11648/j.ajam.20180602.15

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AMA Style

Wang Qin, Zhang Kangkang, Chen Huiting. Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics. Am J Appl Math. 2018;6(2):55-61. doi: 10.11648/j.ajam.20180602.15

Copy | Download

@article{10.11648/j.ajam.20180602.15,
  author = {Wang Qin and Zhang Kangkang and Chen Huiting},
  title = {Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics},
  journal = {American Journal of Applied Mathematics},
  volume = {6},
  number = {2},
  pages = {55-61},
  doi = {10.11648/j.ajam.20180602.15},
  url = {https://doi.org/10.11648/j.ajam.20180602.15},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20180602.15},
  abstract = {In recent years, the issues of the talents introduction have attracted more and more researchers' and college administrators' attention. In the era of big data, data mining technology is widely used in various fields and has achieved remarkable results. The application of data mining technology in the introduction of university talents is in the ascendant. This paper uses the effective information of 245 teachers recruited by Zhejiang University of Finance & Economics since 2011 to explore and model the association rules. It preprocesses the raw information data by hierarchical clustering, and use Apriori algorithm to obtain a set of rules for the paper score and the situation of receiving the National Foundation of China (NFC) in 3 years. These rules will provide a constructive guiding significance for the introduction of talents in Zhejiang University of Finance & Economics.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics
AU  - Wang Qin
AU  - Zhang Kangkang
AU  - Chen Huiting
Y1  - 2018/04/27
PY  - 2018
N1  - https://doi.org/10.11648/j.ajam.20180602.15
DO  - 10.11648/j.ajam.20180602.15
T2  - American Journal of Applied Mathematics
JF  - American Journal of Applied Mathematics
JO  - American Journal of Applied Mathematics
SP  - 55
EP  - 61
PB  - Science Publishing Group
SN  - 2330-006X
UR  - https://doi.org/10.11648/j.ajam.20180602.15
AB  - In recent years, the issues of the talents introduction have attracted more and more researchers' and college administrators' attention. In the era of big data, data mining technology is widely used in various fields and has achieved remarkable results. The application of data mining technology in the introduction of university talents is in the ascendant. This paper uses the effective information of 245 teachers recruited by Zhejiang University of Finance & Economics since 2011 to explore and model the association rules. It preprocesses the raw information data by hierarchical clustering, and use Apriori algorithm to obtain a set of rules for the paper score and the situation of receiving the National Foundation of China (NFC) in 3 years. These rules will provide a constructive guiding significance for the introduction of talents in Zhejiang University of Finance & Economics.
VL  - 6
IS  - 2
ER  -

Copy | Download