The Research On Apriori Algorithm And The Application In The Undergraduate Enrollment Data Mining

Data mining is the process that one can find the unknown and potential knowledge as well as the valuable laws through the abundant data. Apriori Algorithm which digs the potential relation among the information by analyzing and digging the relevance of the data to help decision-making and instruction is the classical one in the association rules. It is widely applied into various fields. With the springing up of the independent colleges as the new power of undergraduate education, the competition among them is also on the increase. Therefore, improving the rate of registration and the quality of students becomes the critical factor to the development of the independent colleges. So it is significant for the enrollment of the independent colleges to find the potential relation between the data which is excavated from the past enrollment information and use it to guide the further enrollment.In this paper, the conception, task and method of data mining are introduced first, emphasizing on the Apriori algorithm of the association rules algorithm. The main parts are as follows:1. The problems of the traditional Apriori and improved Apriori are focused researched and analyzed. And then we put forward two kinds of improved algorithms DPApriori algorithm which is based on the division of data scale and DPApriori-N which is based on the attribute. The effectiveness of the improved algorithms is verified by the experiment.2. We analyze the enrollment data, giving out the rinsing process of the data and the demand analysis of the data mining system.3. We apply the improved Apriori algorithm to the enrollment data mining system, and show the result in a visual way, which can offer decision of recruit and reasonable suggestion to improve the rate of registration and the quality of students.In some sense, it can enhance the quality of running a school.