Understanding the key factors that influence student performances will help students,teachers,and administrators to improve the performance of the students.To this end,the density-based global K-means algorithm is adopted to perform cluster analysis of the student performance data from the UCI machine learning repository for two secondary education Portuguese schools and for a senior middle school of the Pucheng county in the Shaanxi province.The results for the two Portuguese schools reveal that student performance is strongly related to the specific school where the student is enrolled,and location of residence,mother's education level,and if the network is available or not in the family.Education level of the father,the time the student takes on the way to school,the willingness of the student to go to college,and whether the student is in love are factors affecting the student performance to some extent.The results of the third senior middle school demonstrate that student performance is strong related to their guardians,parents' age,parents' education level,learning attitude of the student,and the time the student devotes to courses after classes.In addition,the results indicate that scores of a student for the upcoming examination can be predicted with the available ones and that the predicted scores coincide with the actual ones.The studies in this paper demonstrate that student performance is strong related to parents' education level,especially to mother's education level.The higher the level of education of the mother,the better the student performance.Parents cannot ignore their role in the individual growth of children.It is important to teach students to study actively to improve their achievements.Finally,it is imperative that the education gap between the urban and rural areas is narrowed.