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首页> 外文期刊>Science Journal of Applied Mathematics and Statistics >Application of Association Rule Mining in Talent Introduction Analysis
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Application of Association Rule Mining in Talent Introduction Analysis

机译：关联规则挖掘在人才引进分析中的应用

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摘要

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.
机译：随着高等教育的发展，许多大学越来越重视人才引进。另一方面，关联规则挖掘技术是从复杂数据存储库中提取有用的关联规则的有用方法。本研究以中国浙江财经大学的245名教职员工为例，并使用Apriori算法探索在其获得职称后三年内能否获得中国自然科学基金会（NSFC）的关联规则。被大学录用。这项研究的目的是更好地为大学引进人才，从而提高大学的学术水平。关联规则挖掘的结果表明，发表高质量的论文（如SCI论文和SSCI论文）对学术人员三年内获得NSFC的可能性具有重要影响。此外，博士学位学校的等级也会影响学术人员三年内获得NSFC的可能性。职员的博士学位学校的等级越高，三年内就越容易获得NSFC。

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