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Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.
Clustering Student Data to Characterize Performance Patterns
Digital Object Identifier (DOI) : 10.14569/SpecialIssue.2011.010322
Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence, 2011.
Abstract: Over the years the academic records of thousands of students have accumulated in educational institutions and most of these data are available in digital format. Mining these huge volumes of data may gain a deeper insight and can throw some light on planning pedagogical approaches and strategies in the future. We propose to formulate this problem as a data mining task and use k-means clustering and fuzzy c-means clustering algorithms to evolve hidden patterns.
Keywords: Data mining; k-means Clustering; Fuzzy C-means; Student performance analysis.
Bindiya M Varghese, Jose Tomy J, Unnikrishnan A and Poulose Jacob K, “Clustering Student Data to Characterize Performance Patterns” International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence, 2011. http://dx.doi.org/10.14569/SpecialIssue.2011.010322
@article{Varghese2011,
title = {Clustering Student Data to Characterize Performance Patterns},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA), Special Issue on Artificial Intelligence}
doi = {10.14569/SpecialIssue.2011.010322},
url = {http://dx.doi.org/10.14569/SpecialIssue.2011.010322},
year = {2011},
publisher = {The Science and Information Organization},
volume = {1},
number = {3},
author = {Bindiya M Varghese and Jose Tomy J and Unnikrishnan A and Poulose Jacob K},
}
