基于决策树的就业数据挖掘

针对学生就业问题，给出了就业数据挖掘模型．决策树方法是数据挖掘中非常有效的分类方法，根据就业数据特点，采用了C4．5决策树算法．C4．5算法是决策树核心算法ID3的改进算法，它构造简单，速度较快，容易实现．模型对就业数据预处理，选取决策属性，实现挖掘算法并抽取规则知识，由规则知识指出哪些决策属性决定了就业单位的类别，挖掘结果表明，该算法能够正确将就业数据分类，并得到若干有价值的结论，供决策分析。

Data mining in employment based on decision tree

This paper presents a data mining model to deal with the employment of university graduates. The decision tree is very effective means for cassification, which is proposed according to the characteristics of employment data and C4.5 algorithm. The C4.5 algorithm is improved from ID3 algorithm that is the core algorithm in the decision tree. The C4.5 algorithm is suitable for its simple construction, high processing speed and easy implementation. The model includes preprocess of the data of employment, selection of decision attributes, implementation of mining algorithm, and obtainment of rules from the decision tree. The rules point out which decision attributes decide the classification of employers. Case study shows that this mining algorithm can classify data of employment correctly and find some valuable results for analysis and decision.