Analysis Of University Achievement Based On Data Mining

After entering the era of big data,data mining has been widely applied in various fields.This paper will excavate and analyze the students' achievements in data mining.At present,most colleges and universities in our country analyze the examination paper mainly from the examination paper itself,with the help of simple office software such as excel to make simple statistics on students' performance.Teacher is through the intuitive interface information access to the quality of the papers,students' problem solving situation and students' scores of details,but these intuitive information page only generally include simple average value and standard,the lack of correlation between knowledge points in the work of the paper analysis and mining.Every year there are a lot of questions to school institutions such as management system,which requires to use data mining technology,the data mining analysis,from the mining regularity of master students in such aspects as knowledge.This paper takes the linear algebra achievement of university as an example,and USES the algorithm of clustering and association rules to excavate and analyze it.As a result of the Python language conciseness,readability and scalability,and Python has more special scientific computing extension libraries,so use Python for data mining will have twice the result with half the effort.This paper USES k-means and Apriori algorithm to excavate data.Through the Apriori algorithm,we finally find the knowledge points involved in the big questions in the test paper,such as: solving matrix determinant and solving non-homogeneous linear equations with high correlation and so on.Through kmeans,students can be divided into three categories according to their mastery of knowledge points.Finally,the results obtained from these results obtained the students' mastery of the subject knowledge points in linear algebra,and had some enlightening effects on the teaching contents and methods of the teaching teachers.