Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining
International Journal of Statistical Distributions and Applications
Volume 4, Issue 1, March 2018, Pages: 22-28
Received: Apr. 26, 2018; Published: Apr. 27, 2018
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Authors
Xiao Yang, School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China
Caiyun Ying, School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China
Yefeng Zhou, School of Data Sciences, Zhejiang University of Finance and Economics, Hangzhou, China
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Abstract
Current talent introduction strategies are mainly based on staff arrangement, school discipline construction and so on, which depend on experience actually. However, this kind of empirical approach, lacking of scientific basis, usually causes problems in applications such as uneven scientific research level. In this paper, we intend to use data mining to analyze talent information of teachers in Zhejiang University of Finance and Economics, China from 2011 to 2017, and then to predict their capabilities in obtaining National Foundation of China. In a word, this paper aims to provide decision support for universities’ talent introduction strategies. After data cleaning and feature engineering, Apriori algorithm is applied to mine the association rules and find key factors that are closely related to teachers' acquisition of National Science Foundation of China. Then we make predictions with four kinds of models, including Logistic Regression Model, Decision Tree Model, Artificial Neural Network Model and Support Vector Machine Model. In the end, in order to get a more accurate model, Logistic Regression Model which has the highest accuracy of prediction is used to do stepwise regression.
Keywords
Talent Introduction Strategies, Apriori Algorithm, Prediction Model, R Language
To cite this article
Xiao Yang, Caiyun Ying, Yefeng Zhou, Study on Talent Introduction Strategies in Zhejiang University of Finance and Economics Based on Data Mining, International Journal of Statistical Distributions and Applications. Vol. 4, No. 1, 2018, pp. 22-28. doi: 10.11648/j.ijsd.20180401.13
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