Application Of Decision Tree Technology To Employment Management In Colleges And Universities

With the advancement of policy reform in Colleges and universities,the data volume of college graduates is increasing year by year,and the problem of difficult employment of college graduates is not only the focus of attention of colleges and universities,but also the focus of attention of all sectors of society.Decision tree algorithm is a key and important method in existing data mining.Its rules are simple and easy to understand,and its application value is high.If the decision tree algorithm is applied to the employment management system of colleges and universities,we can grasp the employment law of colleges and universities in the relationship between massive data,so as to provide guidance for the employment of colleges and universities.Firstly,this paper analyses the application performance of traditional C4.5 algorithm in university employment management,realizes the application steps and data processing process of traditional C4.5 algorithm,including raw data acquisition,raw data preprocessing,data preparation,decision tree construction,data mining and so on,and comprehensively evaluates the traditional C4.5 algorithm,and clarifies the existing problems of traditional C4.5 algorithm,mainly including subordinates.Sex missing problem and over fitting problem provide direction for subsequent algorithm optimization.Then,this paper proposes an optimization algorithm of C4.5 based on K-nearest neighbor.Aiming at the problem of attributes missing in traditional C4.5 algorithm,the algorithm fills the missing values with values similar to k-nearest neighbor.On this basis,the improved algorithm is applied to university employment management,and the application effect is evaluated.The results show that the performance of the algorithm based on K-nearest neighbor C4.5 is significantly improved,and it can also solve the problem of over-fitting in traditional C4.5 algorithm.