基于模糊决策树的高校就业数据挖掘研究

Data mining in student's employment based on fuzzy decision tree

随着高等学校招生规模的扩大,大学生就业形势日益严峻。数据挖掘技术可以从大量的历史数据中发现内在的规律和联系。对高校学生就业进行数据挖掘,可发现潜在规律,为就业指导提供决策依据。决策树分类方法是一种有效的数据挖掘方法,但该方法不能很好地处理数据模糊性和不确定性问题。本文提出将模糊决策树算法引入高校就业数据挖掘,解决了数据模糊性和不确定性的问题,生成的知识表示方式自然,易于理解,并且具有更强的分类能力及稳健性。

With the expansion of college and university enrollments, the employment of universi- ty graduates is becoming difficult, and the situation is getting more grim. Mining the student employment-related data, some potential rules and hidden patterns of student employment can be found, so as to guide the decision-making for the provision of careers guidance and to pro- mote the employment of college students. Decision tree classification method is an effective da- ta mining method, but the method can not handle data ambiguity and uncertainty. In this pa- per, the authors present a fuzzy decision tree data mining algorithms into university employ- ment to solve the ambiguity and uncertainty of data problems, and the resulted knowledge is natural and easy to understand, and has a stronger classification ability and robustness.