Association Rule Mining for Career Choices Among Fresh Graduates
Applied and Computational Mathematics
Volume 8, Issue 2, April 2019, Pages: 37-43
Received: May 12, 2019; Published: Jul. 19, 2019
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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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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.
Data Mining, Association Rule Mining, Apriori Algorithm, Career Choice, Academic Performance
To cite this article
Leibao Zhang, Xiaowen Tan, Shuai Zhang, Wenyu Zhang, Association Rule Mining for Career Choices Among Fresh Graduates, Applied and Computational Mathematics. Vol. 8, No. 2, 2019, pp. 37-43. doi: 10.11648/j.acm.20190802.13
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