Data Mining Classification Algorithms Applied Research, Student Information

Since the end of last century,in China the continuous expansion of colleges and universities,resulting in dramatic increase in the number of college and university students,at the same time brought many new problems to the teaching work.Therefore, making an effective use of a large amount of data information in teaching to guide the teaching work scientifically has become particularly important.In this paper,data mining classification techniques is used in the large amount of data information in various aspects of teaching to study higher education,in order to guide teaching scientifically and improve the quality of teaching.Classification is a very important part in Data Mining.There are some famous classified methods,such as Decision Tree,the Bayes and rule induction classification and so on,in which the application of decision tree method is a very wide range.Among them,the Decision Tree exceeds others methods in the feature of well understanding, training,practicability and currency.In this paper,we will focus on using decision tree classification method in teaching and research in colleges and universities.In this paper,Senate's school management software in the analysis of student performance information is taken as an example,according to the implementation of the program,realized the whole process of classification and excavation of the data completely.The process includes making sure the Data Mining target,collecting the data, preprocessing data,classifying and generating the classification rule.ID3 is used to generate a decision tree and post-pruning method is used to pruning the tree.And then according to the decision tree,the classification rule is obtained.In this paper we analysis the concept,arithmetic,the process,tools and other applications of Data Mining.Specially,anglicizing the arithmetic and the application of decision tree ID3,investigating the network of computer learning of the students in Nanshan University,and finding more information for the decision on teaching.