Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics
American Journal of Applied Mathematics
Volume 6, Issue 2, April 2018, Pages: 55-61
Received: Apr. 26, 2018; Published: Apr. 27, 2018
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Authors
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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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.
Keywords
Association Rule Mining, Talents Introduction, Apriori Algorithm
To cite this article
Wang Qin, Zhang Kangkang, Chen Huiting, Association Rule Mining for the Talents Introduction Strategy: A Case Study of Zhejiang University of Finance & Economics, American Journal of Applied Mathematics. Vol. 6, No. 2, 2018, pp. 55-61. doi: 10.11648/j.ajam.20180602.15
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