Students Performance Analysis Using K-Means Algorithm over Decision Tree using Expert System

Authors: Monika ., Prof. Mohit Jain

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Abstract

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 .Kmeans 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.

Introduction

Clustering methods in machine learning have been applied in many applications such as fraud detection, banking, academic performance and instruction detection. Data mining is data analysis methodology used to identify hidden patterns in a large data set. Higher education is very important for student’s life. Higher education institute are focus on analysis of every objects because of private participation. Machine Learning provides various methods these include classification,association,k-means,decision tree, regression, time series, neural network,etc. Application of data mining in the educational system is directly help to analysis of participants in the education system. The students also recommend many activities and task. Many factors could act as a barrier to student for maintaining a high percentage that reflects the overall academic performance in college. These factors could be targeted by the faculty members in developing strategies to improve student learning and academic performance by the way of monitoring and analyzing the progression of their performance. Data mining is also used to show how students use material of particular course. In teaching environment trainer are able to obtain feedback on students.

Conclusion

Machine learning is very emerging technology that every placed it used. Now days in bank, labs, telecom, industrial each and every place machine learning is used. Data mining is part of it which helps in prediction, future prediction is very important in many place which help so much. Many algorithm is build and more and more research is going on every technology used the concept of it. We survey many papers for prediction of students’ performance.Decision tree method is used in many place but on comparing to clustering techniques i.e. k means it is less efficient, K means is more efficient and stable.Students’performance is so important for their future it not only help student but also help teachers institute parents. Many big institutes used the concept of AI for prediction.