Research On The Application Of Apriori Algorithm In The Analysis Of Student Achievement

Data mining technology, which is a kind of new data analysis technology as well as a kind of technique in data in the sea to find the useful knowledge in the service of mankind, is the inevitable result of t he development of information technology. The method of mining association rules is one of the methods of data mining method is very important. And the most classical association rules algorithm is the Apriori algorithm, the algorithm in many areas（such a s finance, commercial areas, etc.） has a corresponding application, and achieved good results.The core task of colleges and universities is teaching, improve teaching quality is the fundamental, however the performance of students is an important indicato r to measure the level of teaching quality. Therefore, the student achievement data related to data mining processing has important practical significance. Relationship between different courses, various factors influencing student achievement can be found by using data mining technology to mine student achievement data, these information can not only provide a powerful decision support for teaching and related management work, but also better development of teaching and provide a scientific basis for improving teaching quality.This paper is mainly focused on the method of association rules mining technology based on further research, and special attention is given to the analysis of the classical association rules algorithms: Apriori algorithm, which poin ts out the advantages and disadvantages of the algorithm and presents an optimization Apriori algorithm based on the tag of things.After analyzing the efficiency of the optimization Apriori algorithm based on the tag of things, this paper presents another kind of further optimization Apriori algorithm ——Apriori₃3 algorithm, which is proposed three major optimization improvements: the first is about optimization of the data storage; the second is about optimization of the item set before the connection, the third is about optimization of the item set at the time of connection. And finally the Apriori₃3 algorithm is going to be applied to the student achievement data mining.In this paper, using MS SQL SERVER 2008 as the background database and Visual Studio 2010 as the development tool for the association rule mining of student achievement data stored in the database has got better results. The results show that the existence of some correlations between courses, the performance of a or a few courses will be likely to have a direct effect on that of the other subsequent course. Of course, course sequence may also affect student achievement, such information can not only provide a powerful decision support for teaching and related management work, but also better development of teaching and provide a scientific basis for improving teaching quality.