Application Of Decision Tree In The University Achievement Analysis

Due to successive years of enrollment of the university, every school has accumulateda large number of students’ basic data and achievement data, but these data are still stuck inthe backup, the query, the stage of simple statistics, and can not play its due role.This led to alot of performance data is only stored on different computers vary in tabular form, and thosedata can not conducive to the information implied by these data mining analysis,can not tofacilitate the teaching departments for effective teaching evaluation, and can not conduciveto comprehensive guidance teachers brought student learning.In this thesis,through the analyze of the college students’achievements based on decisiontree technique,we get the factors of identify the achievements of college students affect"excellent" and "bad".in creating a classification model, we choose the the rate of informationgain as attribute selection metrics.we use C4.5algorithm to set to the model for the scores are“excellent”,and generate the classification rules and obtain the factors of the studentperformance as "excellent". C4.5algorithm used the gainrate of information as the attributeselection metrics,and related to the logarithm in calculating the amount of information,then itis inefficient in time.Here,I improve the formula for calculation of property the amount ofinformation and form a new method of calculate information. In creating a classificationmodel, I choose the the rate of information gain as attribute selection metrics.i use new C4.5algorithm to set to the model for the scores are “bad”,and generate the classification rules andobtain the factors of the student performance as "bad".those data provides a strongsupporting to improve the quility of teaching for me.