Data mining in the academic performance of self – financing arts and science college students using K-Means clustering algorithm - Senthil Kumar, R; Arulanandam, K (International Journal of Computer Sciences and Engineering, 2025)
To impart quality education and to improve the quality of managerial decisions are the main objective of any higher educational institution and also to reduce the drop out ratio to a significant level and to improve the performance of students. To apply data mining techniques by weka software for the academic performance related variables are analyzed. To segment students into groups according to their characteristics cluster analysis was used in this study. This includes the student’s socio economic characters, skill development characters, motivational characters and infrastructural facilities. The application technique will help to classify the best performance of students. The academic performance of 1398 self – financing arts and science college students were selected during their final year of the study. The useful information and related attributes were stored in Educational database and to extract meaningful information and to develop the significant relationship clustering methods were used in this paper. To enhance the quality of educational system by analyzing and improving student’s best performance related characters were identified.