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

| Peer-Reviewed

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 Accepted: Published: 19 July 2019

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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), 2024. Published by Science Publishing Group

Keywords

Data Mining, Association Rule Mining, Apriori Algorithm, Career Choice, Academic Performance

References

[1]	Gao, L. (2015). Analysis of employment data mining for university student based on WEKA platform. Journal of Applied Science and Engineering Innovation, 2 (4), 130-133.
[2]	Mishra, T., Kumar, D., & Gupta, S. (2016). Students’ employability prediction model through data mining. International Journal of Applied Engineering Research, 11 (4), 2275-2282.
[3]	González-Romá, V., Gamboa, J. P., & Peiró, J. M. (2018). University graduates’ employability, employment status, and job quality. Journal of Career Development, 45 (2), 132-149.
[4]	Chen, M. S., Han, J., & Yu, P. S. (1996). Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8 (6), 866-883.
[5]	Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33 (1), 135-146.
[6]	Solanki, S. K., & Patel, J. T. (2015). A survey on association rule mining. In Proceedings of the 5th International Conference on Advanced Computing & Communication Technologies, February 21-22, Haryana, India, pp. 212-216.
[7]	Ho, G. T., Ip, W. H., Wu, C. H., & Tse, Y. K. (2012). Using a fuzzy association rule mining approach to identify the financial data association. Expert Systems with Applications, 39 (10), 9054-9063.
[8]	Kumar, S., & Toshniwal, D. (2016). A data mining approach to characterize road accident locations. Journal of Modern Transportation, 24 (1), 62-72.
[9]	Mane, R. V., & Ghorpade, V. R. (2016). Predicting student admission decisions by association rule mining with pattern growth approach. In Proceedings of International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques, December 9-10, Mysuru, India, pp. 202-207.
[10]	Nahar, J., Imam, T., Tickle, K. S., & Chen, Y. P. P. (2013). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40 (4), 1086-1093.
[11]	Shi, F., Sun, S., & Xu, J. (2012). Employing rough sets and association rule mining in KANSEI knowledge extraction. Information Sciences, 196, 118-128.
[12]	Tyagi, S., & Bharadwaj, K. K. (2013). Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining. Swarm and Evolutionary Computation, 13, 1-12.
[13]	Wang, J., Li, H., Huang, J., & Su, C. (2016). Association rules mining based analysis of consequential alarm sequences in chemical processes. Journal of Loss Prevention in the Process Industries, 41, 178-185.
[14]	Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Data, May 26-28, Washington D. C., USA, pp. 207-216.
[15]	D’Angelo, G., Rampone, S., & Palmieri, F. (2017). Developing a trust model for pervasive computing based on Apriori association rules learning and Bayesian classification. Soft Computing, 21 (21), 6297-6315.
[16]	Deng, X., Zeng, D., & Shen, H. (2018). Causation analysis model: Based on AHP and hybrid Apriori-Genetic algorithm. Journal of Intelligent & Fuzzy Systems, 35 (1), 767-778.
[17]	Guo, Y., Wang, M., & Li, X. (2017). Application of an improved Apriori algorithm in a mobile e-commerce recommendation system. Industrial Management & Data Systems, 117 (2), 287-303.
[18]	Ilayaraja, M., & Meyyappan, T. (2013). Mining medical data to identify frequent diseases using Apriori algorithm. In Proceedings of International Conference on Pattern Recognition, Informatics and Mobile Engineering, February 21-22, Salem, India, pp. 194-199.
[19]	Nair, J. J., & Thomas, S. (2017). Improvised Apriori with frequent subgraph tree for extracting frequent subgraphs. Journal of Intelligent & Fuzzy Systems, 32 (4), 3209-3219.
[20]	Prasanna, S., & Ezhilmaran, D. (2016). Association rule mining using enhanced Apriori with modified GA for stock prediction. International Journal of Data Mining, Modelling and Management, 8 (2), 195-207.
[21]	Agrawal, R., Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th VLDB Conference, September 12-15, Santiago, Chile, pp. 487-499.
[22]	Edwards, K., & Quinter, M. (2011). Factors influencing students career choices among secondary school students in Kisumu Municipality, Kenya. Journal of Emerging Trends in Educational Research and Policy Studies, 2 (2), 81-87.
[23]	Uyar, A., Güngörmüs, A. H., & Kuzey, C. (2011). Factors affecting students’ career choice in accounting: The case of a Turkish university. American Journal of Business Education, 4 (10), 29-38.
[24]	Lent, R. W., Brown, S. D., Talleyrand, R., McPartland, E. B., Davis, T., Chopra, S. B., Alexander, M. S., Suthakaran, V., & Chai, C. M. (2002). Career choice barriers, supports, and coping strategies: College students’ experiences. Journal of Vocational Behavior, 60 (1), 61-72.
[25]	Wye, C. K., & Lim, Y. M. (2009). Perception differential between employers and undergraduates on the importance of employability skills. International Education Studies, 2 (1), 95-105.
[26]	Bridgstock, R. (2009). The graduate attributes we’ve overlooked: Enhancing graduate employability through career management skills. Higher Education Research & Development, 28 (1), 31-44.

Cite This Article

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

Copy | Download

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

Copy | Download

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  -

Copy | Download

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

Download PDF

Sections

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

Copy | Download

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

Copy | Download

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  -

Copy | Download