Association Rule Mining for Career Choices Among Fresh Graduates

Leibao Zhang; Xiaowen Tan; Shuai Zhang; Wenyu Zhang

doi:doi:10.11648/j.acm.20190802.13

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Association Rule Mining for Career Choices Among Fresh Graduates

Leibao Zhang, Xiaowen Tan, Shuai Zhang, Wenyu Zhang

Published in Applied and Computational Mathematics (Volume 8, Issue 2)

Received: 12 May 2019 Published: 19 July 2019

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Abstract

Nowadays, an increasing number of colleges have built information systems to manage masses of educational data, but actually most data is in an idle state and fails to create any value. As an efficient data analysis method, association rule mining can precisely make good use of these disordered data and extract useful but latent information from them. In this paper, an example of 228 students, who graduated from the School of Information of Zhejiang University of Finance and Economics, China in 2017, is taken to discover the association rules between their career choices and academic performance using Apriori algorithm. The main purpose of this paper is to offer fresh graduates a reference to future career choices and help teachers guide them in better career planning. The experimental results indicate that the courses students are good at largely affect their career choices, although their overall career scope is not narrow.

Published in	Applied and Computational Mathematics (Volume 8, Issue 2)
DOI	10.11648/j.acm.20190802.13
Page(s)	37-43
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, Career Choice, Academic Performance

References

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APA Style

Leibao Zhang, Xiaowen Tan, Shuai Zhang, Wenyu Zhang. (2019). Association Rule Mining for Career Choices Among Fresh Graduates. Applied and Computational Mathematics, 8(2), 37-43. https://doi.org/10.11648/j.acm.20190802.13

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ACS Style

Leibao Zhang; Xiaowen Tan; Shuai Zhang; Wenyu Zhang. Association Rule Mining for Career Choices Among Fresh Graduates. Appl. Comput. Math. 2019, 8(2), 37-43. doi: 10.11648/j.acm.20190802.13

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AMA Style

Leibao Zhang, Xiaowen Tan, Shuai Zhang, Wenyu Zhang. Association Rule Mining for Career Choices Among Fresh Graduates. Appl Comput Math. 2019;8(2):37-43. doi: 10.11648/j.acm.20190802.13

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@article{10.11648/j.acm.20190802.13,
  author = {Leibao Zhang and Xiaowen Tan and Shuai Zhang and Wenyu Zhang},
  title = {Association Rule Mining for Career Choices Among Fresh Graduates},
  journal = {Applied and Computational Mathematics},
  volume = {8},
  number = {2},
  pages = {37-43},
  doi = {10.11648/j.acm.20190802.13},
  url = {https://doi.org/10.11648/j.acm.20190802.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20190802.13},
  abstract = {Nowadays, an increasing number of colleges have built information systems to manage masses of educational data, but actually most data is in an idle state and fails to create any value. As an efficient data analysis method, association rule mining can precisely make good use of these disordered data and extract useful but latent information from them. In this paper, an example of 228 students, who graduated from the School of Information of Zhejiang University of Finance and Economics, China in 2017, is taken to discover the association rules between their career choices and academic performance using Apriori algorithm. The main purpose of this paper is to offer fresh graduates a reference to future career choices and help teachers guide them in better career planning. The experimental results indicate that the courses students are good at largely affect their career choices, although their overall career scope is not narrow.},
 year = {2019}
}

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TY  - JOUR
T1  - Association Rule Mining for Career Choices Among Fresh Graduates
AU  - Leibao Zhang
AU  - Xiaowen Tan
AU  - Shuai Zhang
AU  - Wenyu Zhang
Y1  - 2019/07/19
PY  - 2019
N1  - https://doi.org/10.11648/j.acm.20190802.13
DO  - 10.11648/j.acm.20190802.13
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 37
EP  - 43
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20190802.13
AB  - Nowadays, an increasing number of colleges have built information systems to manage masses of educational data, but actually most data is in an idle state and fails to create any value. As an efficient data analysis method, association rule mining can precisely make good use of these disordered data and extract useful but latent information from them. In this paper, an example of 228 students, who graduated from the School of Information of Zhejiang University of Finance and Economics, China in 2017, is taken to discover the association rules between their career choices and academic performance using Apriori algorithm. The main purpose of this paper is to offer fresh graduates a reference to future career choices and help teachers guide them in better career planning. The experimental results indicate that the courses students are good at largely affect their career choices, although their overall career scope is not narrow.
VL  - 8
IS  - 2
ER  -

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Author Information

Leibao Zhang

School of Public Finance and Taxation, Zhejiang University of Finance and Economics, Hangzhou, China
Xiaowen Tan

School of Public Finance and Taxation, Zhejiang University of Finance and Economics, Hangzhou, China
Shuai Zhang

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Wenyu Zhang

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

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Plain Text BibTeX RIS

APA Style

Leibao Zhang, Xiaowen Tan, Shuai Zhang, Wenyu Zhang. (2019). Association Rule Mining for Career Choices Among Fresh Graduates. Applied and Computational Mathematics, 8(2), 37-43. https://doi.org/10.11648/j.acm.20190802.13

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ACS Style

Leibao Zhang; Xiaowen Tan; Shuai Zhang; Wenyu Zhang. Association Rule Mining for Career Choices Among Fresh Graduates. Appl. Comput. Math. 2019, 8(2), 37-43. doi: 10.11648/j.acm.20190802.13

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AMA Style

Leibao Zhang, Xiaowen Tan, Shuai Zhang, Wenyu Zhang. Association Rule Mining for Career Choices Among Fresh Graduates. Appl Comput Math. 2019;8(2):37-43. doi: 10.11648/j.acm.20190802.13

Copy | Download

@article{10.11648/j.acm.20190802.13,
  author = {Leibao Zhang and Xiaowen Tan and Shuai Zhang and Wenyu Zhang},
  title = {Association Rule Mining for Career Choices Among Fresh Graduates},
  journal = {Applied and Computational Mathematics},
  volume = {8},
  number = {2},
  pages = {37-43},
  doi = {10.11648/j.acm.20190802.13},
  url = {https://doi.org/10.11648/j.acm.20190802.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.20190802.13},
  abstract = {Nowadays, an increasing number of colleges have built information systems to manage masses of educational data, but actually most data is in an idle state and fails to create any value. As an efficient data analysis method, association rule mining can precisely make good use of these disordered data and extract useful but latent information from them. In this paper, an example of 228 students, who graduated from the School of Information of Zhejiang University of Finance and Economics, China in 2017, is taken to discover the association rules between their career choices and academic performance using Apriori algorithm. The main purpose of this paper is to offer fresh graduates a reference to future career choices and help teachers guide them in better career planning. The experimental results indicate that the courses students are good at largely affect their career choices, although their overall career scope is not narrow.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Association Rule Mining for Career Choices Among Fresh Graduates
AU  - Leibao Zhang
AU  - Xiaowen Tan
AU  - Shuai Zhang
AU  - Wenyu Zhang
Y1  - 2019/07/19
PY  - 2019
N1  - https://doi.org/10.11648/j.acm.20190802.13
DO  - 10.11648/j.acm.20190802.13
T2  - Applied and Computational Mathematics
JF  - Applied and Computational Mathematics
JO  - Applied and Computational Mathematics
SP  - 37
EP  - 43
PB  - Science Publishing Group
SN  - 2328-5613
UR  - https://doi.org/10.11648/j.acm.20190802.13
AB  - Nowadays, an increasing number of colleges have built information systems to manage masses of educational data, but actually most data is in an idle state and fails to create any value. As an efficient data analysis method, association rule mining can precisely make good use of these disordered data and extract useful but latent information from them. In this paper, an example of 228 students, who graduated from the School of Information of Zhejiang University of Finance and Economics, China in 2017, is taken to discover the association rules between their career choices and academic performance using Apriori algorithm. The main purpose of this paper is to offer fresh graduates a reference to future career choices and help teachers guide them in better career planning. The experimental results indicate that the courses students are good at largely affect their career choices, although their overall career scope is not narrow.
VL  - 8
IS  - 2
ER  -

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