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在高职教育逐渐普及的背景下,学院计算机网络专业毕业生就业竞争力研究对专业发展意义重大,数据挖掘中的分类算法为此项研究提供了现实可能。本文对数据挖掘的技术路线包括数据挖掘、分类算法、算法的选取和优化进行了阐述;针对所收集到的数据特点,提出选择分类算法中的决策树算法更加适宜研究该专业毕业生就业竞争力情况。应用决策树算法原理对数据进行实例分析,依据C4.5算法构造决策树,分析实验结果,将与人交往能力、社会工作经历、专业知识等七个维度依次排序,推导出高职计算机网络专业毕业生非专业因素和综合素质对于提升就业竞争力影响深刻的结论。
Under the background of gradual popularization of higher vocational education, the research on the employment competitiveness of college computer network major graduates is of great significance to professional development. The classification algorithm in data mining provides a realistic possibility for this research. In this paper, the technical route of data mining including data mining, classification algorithm, algorithm selection and optimization are described. According to the characteristics of the collected data, the decision tree algorithm in selecting classification algorithm is more suitable to study the employment competitiveness of the graduates Happening. Applying the principle of decision tree algorithm to analyze the data, constructing the decision tree according to C4.5 algorithm, analyzing the experimental results, sorting the seven dimensions of human communication ability, social work experience and professional knowledge in sequence, and deduced the higher vocational computer network specialty The conclusion that non-professional and comprehensive qualifications of graduates have a profound impact on enhancing the competitiveness of employment.