Students Academic Performance Analysis by using Apriori Technique

Educational data mining (EDM) is a multi-disciplinary research area that examines statistical modeling, artificial intelligence and data mining with the data generated from an educational institution. In this mainly focus on prediction of student’s academic performance. Some statistical tools are also used for predicting student’s performance. In this paper we implemented Apriori and k-means clustering algorithm is use for predicting the student’s result. Apriori algorithm more efficient and less time over whelming for predicting the student results. The main objective of exploitation Apriori and k-means clustering algorithm is for predicting the students’ academic performance and also improving students’ academic performance. During this academic student performance is evaluated supported some attributes are selected which generate rules by suggests that of association rule. In this paper we used k-means algorithm for calculating the test anxiety (stress) and time management of the students at the time of examination. It will help to improve the student’s tutorial performance. Experiment is conducted usingWeka and real time knowledge set on the market within the college premises , collected from the students and the result which released by the university.