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Cluster-based distributed architecture for prediction of student’s performance in higher education

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Abstract

Educational data mining (EDM) has emerged as a research area in recent years for researchers all over the world from different and related research areas. The EDM obtained knowledge can be used to offer suggestions to the academic planners in higher education institutes to enhance their decision-making process. Literature has suggested various prediction models for predicting the student’s performance. This work proposes the cluster based distributed architecture for predicting the student’s performance. The proposed cluster-based distributed architecture performs the prediction with clustering through Bayesian fuzzy clustering, feature extraction through Kernel-based principal component analysis, and prediction through the proposed Lion–Wolf based deep belief network (LW-DBN). The proposed architecture uses the LW algorithm to find the optimal weights for the DBN. The experimentation of the proposed work is done by collecting a real-time database and measuring the prediction performance through the mean square error (MSE) and root MSE (RMSE). The proposed LW-DBN model has achieved lower error performance than other models with MSE and RMSE values of 0.222606 and 0.050435, for the database.

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Authors and Affiliations

  1. SCOPE, VIT, Vellore, Tamil Nadu, 632014, India

    L. Ramanathan, K. Vijayakumar & S. Ramani

  2. Jeppiaar Maamallan Engineering College, Jeppiaar Nagar, Sriperumpudur, Kancheepuram, 602 108, India

    G. Parthasarathy

  3. Department of CSE, School of Computing, Sathyabama University, Chennai, Tamil Nadu, 600119, India

    L. Lakshmanan

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  1. L. Ramanathan
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  2. G. Parthasarathy
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  3. K. Vijayakumar
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  4. L. Lakshmanan
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  5. S. Ramani
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Correspondence to L. Ramanathan.

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Ramanathan, L., Parthasarathy, G., Vijayakumar, K. et al. Cluster-based distributed architecture for prediction of student’s performance in higher education. Cluster Comput 22 (Suppl 1), 1329–1344 (2019). https://doi.org/10.1007/s10586-017-1624-7

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  • DOI: https://doi.org/10.1007/s10586-017-1624-7

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