The Application Of Decision Tree Technology In The Employment Analysis System Of Colleges And Universities

With the deepening of the innovation of higher education, the scale of college enrollment expanded annually and the increasing of the number of students, Making the employment situation tougher and tougher?Not only does the employees focus on the personal abilities of the graduates, but it is a great challenge to the career development and the management class.For this situation,how to develop corresponding employment guidelines is now the need for each university to consider. Many universities stored the data of employment for a long time,however, this data has not been implied efficiently. In this thesis, applying the decision tree in data mining technology to excavate the information of graduates, digging out the relationship between graduates and employments, provide significant theoretical basis for the employments.Data mining techniques extracts data from large excavated data which has essential potential implement. The main algorithms of data mining techniques are as follows: the decision tree algorithm, neural network algorithm, association rule algorithm, statistical analysis method and fuzzy set method. In this paper, according to the characteristics of the graduates data,ID3 of the decision tree algorithm, which adjust the concept of correction coefficient to modify the algorithm. By the analysis of improved ID3 algorithm based on correction function and attribute priority value, introducing the concept of correction factor,optimizing the time complexity and accuracy of the algorithm, and through examples to verify The improved ID3 algorithm is more suitable for graduate data mining.In this thesis, decision tree algorithm is utilized to analysis the employment data, which is based on the data of the Computer Science and Engineering from 2012.The classification of data mining process is achieved. Including the identifying targets and goals of data mining, data acquisition, data integration, data cleaning up, data conversion, data pre-processing technology and so on. Imply the modified ID3 algorithm based on the correction factor to obtain the classification of decision tree, which create decision tree classification rules. Excavate several rules related to university students' employment after the student employment analysis model is settled. It is of great practical significance to help college to improve the employment guidance of graduates, improve the employment rate and the quality of employment.