摘要
为了提高高职院校学生的就业率,需要对其就业情况进行预测,找到影响就业的主要因素。利用数据挖掘技术中的聚类分析智能技术来分析高职院校数据库中的历届毕业生的数据,通过分析的结果对今后就业的情况进行预测。选择使用了聚类技术中的基于模型的方法,通过建立的分类树能够找到影响就业的主要因素。高职院校的就业管理的相关领导可以根据分类结果进行决策,有利于提高高职院校的就业率。
In order to improve the employment percent of students in higher vocational colleges, it is necessary to predict their employment situation and find out the main factors that affect their employment. This paper uses the cluster analysis intelligent technology of data mining technology to analyze the data of graduates in the database of higher vocational colleges, and forecasts the employment situation in the future through the analysis results. By using the model-based method of clustering technology, the main factors affecting employment can be found through the established classification tree. The relevant leaders of employment management in higher vocational colleges can make decisions according to the classification results, which is conducive to improving the employment percent of higher vocational colleges.
作者
李世科
LI Shi-ke(Henan Institute of Economics and Trade,Zhengzhou 450018,Henan)
出处
《电脑与电信》
2020年第3期59-61,68,共4页
Computer & Telecommunication
关键词
智能技术
聚类分析
就业率
预测
intelligent technology
cluster analysis
employment rate
prediction
作者简介
李世科(1982-),女,河南汤阴人,本科,讲师,研究方向为程序设计、数据库、计算机基础。
