Research On The Association Rules Algorithm Applied In The Students’ Quality Evaluation System

Mining potential knowledge and value of data on student information, helping policy makers to manage and adjust, then nurturing personalized talent of new era efficiently is one of the important directions of the research on the student information management at present. Association can be found in a large amount of data in students’ grades, activities, practices, psychology and so on by using association rules mining algorithm, to reveal the problems existing in training and teaching students. According to the mining results, people can predict the growth of students, make scientific evaluation on students, and fully consider the characteristics of different students. Then we can provide a more personalized education for each student according to his character, learning situation and daily routine.In this context, the paper focuses on improving association rules mining algorithm, improving the evaluation model and researching the students’ quality evaluation system on the basis of the improved model. Main research contents can be summarized as follows:Above all, study and research association rules mining algorithm, analyze the advantages and disadvantages of FP- growth algorithm, improve the existing efficient algorithm in the aspect of incremental mining according to the characteristics of the students’ quality evaluation system database. At last, compare new algorithm with the existing incremental mining algorithm. The experimental results show that the improved algorithm in the incremental mining is accurate and more efficient.Secondly, analyze the students’ quality evaluation indexes. Calculate weights of students’ quality evaluation indexes by using AHP which based on G1 algorithm. Then build the students’ quality evaluation model. G1 algorithm can simple the calculation process of AHP and avoid possible bias in the result. So that the weighting calculation can be more efficient and the weights of indexes can be more scientific and accurate. Thus we can provide a more reliable guarantee to get a scientific evaluation result.Finally, design and achieve the students’ quality evaluation system according to the quality evaluation model constructed above, then get the data of student evaluation of in recent years. After the preprocessing of the data, discover the potential rules among the qualities of student by the improved algorithm. Experiments prove that the existence of the various potential association rules among moral education, intellectual education, innovation, style and comprehensive evaluation. These rules can be applied to teaching management in the usual, to provide objective and scientific guidance for the students and administrators. Guide the students all-round development and Guide manager adjust means and direction timely.