Science Journal of Applied Mathematics and Statistics
Volume 7, Issue 3, June 2019, Pages: 45-50
Received: Aug. 5, 2019;
Published: Sep. 27, 2019
Views 493 Downloads 65
Zixuan Chen, School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Jiepin Ding, School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Zhiguang Zhou, School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Yin Zhu, School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Wenyu Zhang, School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
With the advancement of higher education, many colleges have given increasing attention to talent introduction. On the other hand, the association rule mining technique is a useful method which extracts the useful association rules from the complex data repositories. This study takes the example of 245 academic staff from Zhejiang University of Finance and Economics, China and uses Apriori algorithm to explore the association rules on whether an academic staff can obtain the Natural Science Foundation of China (NSFC) within three years after s/he is recruited to the university. The aim of this study is to better introduce talents for colleges so that the academic levels of colleges can be improved. The results of association rule mining have shown that having published high quality papers such as SCI paper and SSCI paper has an important effect on the probability of academic staff to obtain NSFC within three years. Besides, the grade of PhD school has also an effect on the probability of academic staff to obtain NSFC within three years. The higher the grade of a staff’s PhD school is, the easier for him to obtain NSFC within three years.
Application of Association Rule Mining in Talent Introduction Analysis, Science Journal of Applied Mathematics and Statistics.
Vol. 7, No. 3,
2019, pp. 45-50.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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