Implementation of Students’ Performance Using Modified K-Means Algorithm over Decision Tree

Machine Learning is a field that is used in every system. Machine learning is used in educational system, in pattern recognition, Games, Industries. In education system its importance becomes more because of the future of the students. Education data mining is very useful disciplines, because the amount of data in education system is increasing day by day .in higher education is relatively new but its importance increases because of increasing database. There are many approach for measuring students’ performance .K-means is one of most efficient and used method .With the help of data mining the hidden information in the database is get out which help for improvement of students’ performance. Decision tree is also a method used to predict the students’ performance. In recent years, the biggest challenges that Educational institutions are facing the more growth of data and to use this data to improve the quality so it can take better decisions. Clustering is one of the basic techniques often used in analyzing data sets. This study makes use of cluster analysis to segment students in to groups according to their characteristics. Unsupervised algorithm like K-means is discussed. Education data mining is used to study the data available in education field to bring the hidden data i.e. important and useful information from it. With the help of these it is easy to improve the result and future of students.