Data Mining and Knowledge Engineering

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Student Performance and Placement Prediction Using Cluster and Association Techniques


Affiliations
1 Department of Computer Science, Government Arts College, Coimbatore, Tamilnadu, India
2 Department of Information Technology, Government Arts College, Coimbatore, Tamilnadu, India
     

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One of the major concerns of higher educational teaching is evaluating and enhancing the learning institution. The most important goal of higher education institutions is to present excellence education and high placement result to its students. College deals including huge amount of electronic records connected to student’s details, results, attendance records, placements information and many others. The efficient goal of several higher educational institutions is to increase the excellence of learning and placement. In this research we would like to focus weak and intelligence student’s analyses using k-means clustering and Apriori algorithm to bring the clarity in student’s details, placement details and student results. The K-means clustering and Apriori algorithm is used to analysis and improves student quality of education and placement. In this paper the clustering and association algorithms are used to analysis student details using WEKA tool.

Keywords

Academic Analytics, Apriori Algorithm, Educational Data Mining, K-Means Clustering.
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Abstract Views: 281

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  • Student Performance and Placement Prediction Using Cluster and Association Techniques

Abstract Views: 281  |  PDF Views: 2

Authors

Selinrachel
Department of Computer Science, Government Arts College, Coimbatore, Tamilnadu, India
T. Christopher
Department of Information Technology, Government Arts College, Coimbatore, Tamilnadu, India

Abstract


One of the major concerns of higher educational teaching is evaluating and enhancing the learning institution. The most important goal of higher education institutions is to present excellence education and high placement result to its students. College deals including huge amount of electronic records connected to student’s details, results, attendance records, placements information and many others. The efficient goal of several higher educational institutions is to increase the excellence of learning and placement. In this research we would like to focus weak and intelligence student’s analyses using k-means clustering and Apriori algorithm to bring the clarity in student’s details, placement details and student results. The K-means clustering and Apriori algorithm is used to analysis and improves student quality of education and placement. In this paper the clustering and association algorithms are used to analysis student details using WEKA tool.

Keywords


Academic Analytics, Apriori Algorithm, Educational Data Mining, K-Means Clustering.

References