Based On Data Mining Technology Decision-making Support System

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.National College Entrance Examination (NCEE), which influences many families, has been a focus of the society. After annual college entrance examination, mass of data is just to be stored for simple inquiry. Year after year, the potential value of the mass data remains to be identified. Networking and information digitization is changing the people's life. The students and their parents cannot be satisfied with the surface layer data such as score, acceptance points, etc. any longer. They also want to get individualized and high-quality information service.Based on the analysis of primitive NCEE data in the database, Data Warehouse (DW), On-line Analytical Processing(OLAP) and Data Mining(DM), this thesis develops a new form of Decision Support System(DSS), which attempts to provide society with some helpful efficient and exact decision making support.This article introduces the architecture of the DSS supported by the DW, OLAP and DM, the function and concepts of DW, and DM as well as their applications, introduces the DSS design of NCEE, discusses such key questions in the design as the system structure, the system data warehouse, the system function modeling, and so on, in order to lay a good basis for the perfect functioning of the system, and analyzes the key technology and algorithm as regards the score standardization, the multi-purpose decision strategy, the Decision Tree algorithm, followed by a confirmation and appraisal of the testing results of the system.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.The findings indicate that the DSS of NCEE supported by the DW, OLAP and DM has some significant implications in both theory and application.