The Research On Application Of Data Classification In Teaching Of High Learning

As there are large amounts of data in the databases, it is very important for us to find the useful information from the database, and the Data Mining technology is an efficient solution to this problem. The research of Data Mining has reached significant achievement and has been applied successfully in many areas. However, successful application of Data Mining in the field of education has not been reported.With popularization of higher education, more and more students come into the university and it brings us many new problems. The teachers want to analyze large numbers of data scientifically, which including student's result was brought in the process of teaching. They want to obtain great benefits for the education. After analyzing the feasibility of using Data Mining technique in the area of higher education, this paper gives a project to achieve it.Classification is a very important task in Data Mining. It builds a model according to the data whose class labels are known, and then uses this model to predict the classes of the data whose class labels are unknown. There are some famous classified algorithms such as Decision Tree, the Bayes, and the Neural Network. Among them, the Decision Tree exceeds the others in the feature of well understanding, well training and achievable. In this paper, we select the Decision Tree classified method in the application of higher education.According to the project, we accomplish the Data Mining process to analyze the student's result. The process includes making sure the Data Mining target, collecting the data, preprocessing data, classifying and generating the classification rule. We use ID3 algorithms to generate a decision tree, use postpruning method to pruning the tree. And then according to the decision tree, we obtain the classification rule.In the course of researching, we accomplish a Decision Tree classifier. The classifier is simple but it has a nice interface. It can open data file, generate Decision Tree, prune the branch, create the classification rule, and save the result. The experiment shows that the classifier can successfully build decision trees and has a...