ANALYSIS OF STUDENTS PERFORMANCE METHODS USING MODIFIED K-MEANS MODEL

Rahul Kaul

Abstract


:- Machine Learning is a field of artificial intelligence that can use past information for the future purpose. Machine learning is similar to data mining in the way that both are looking for the pattern. Machine learning can detect a pattern in data and adjust the action. Machine learning is a field that is used in every system. Machine learning is used in the educational system, pattern recognition, Games, Industries, Social media services, online customer support, Product Recommendation Etc. In the education system, its importance becomes more because of the future of the students. There is a huge amount of data in higher education because nowadays every student is looking for higher education, so there is more need for machine learning methods in the education system. Many methods are there for the analysis of the students performance. Hidden information is carried out with the use of data mining, which will help in the analysis of students information.

There is a huge amount of data in education and all the data are useful for students as well as for teachers. With the growth of institute, it becomes more importance of machine learning technology in the educational field. Clustering is one of the basic techniques often used in analyzing data sets. Many clustering techniques are there but modified K-means is one of most efficient and used method. Classification techniques are also there and the most popular is the decision tree. A decision tree is also a method used for analysis of the students performance but compared to modified K-means, it is less stable. The unsupervised algorithm is discussed. These make use of cluster analysis to segment students into groups according to their characteristics. Elbow method is there to determine the cluster size; it will help in the optimal solution. Elbow method looks over the arm and elbow point is there. With the help of machine learning concept, it is easy to improve the result and future of students. It is not only useful for students but also for teacher and institute to improve their result.

Keywords: -Data mining, EDM, K-means, Decision Tree, Students data.


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References


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