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Abstract

Educational data mining (EDM) transforms raw data from education systems into actionable insights that can significantly impact research and academic performance. This paper explores how universities can leverage academic data for strategic purposes by conducting a comparative study of clustering algorithms applied in academic data mining. It compares partition-based (K-Means), density-based (DBSCAN), and hierarchical (BIRCH) methods to determine the most suitable technique for clustering analysis in educational settings. The results indicate that the classification-based K-Means algorithm outperforms the hierarchical BIRCH algorithm and the density-based DBSCAN algorithm.

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Algorithm Clustering Data Mining EDM Technique

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Bahrami, A. Z., & Shahidzay, A. K. (2025). Comparative Study of Clustering Algorithms in the Context of Education Data Mining. Journal of Natural Sciences – Kabul University, 5(3), 61–70. https://doi.org/10.62810/jns.v5i3.295
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References

  1. Martins MPG, Miguéis VL, Fonseca DSB. Data Mining Educacional: Uma Revisão da Literatura Educational Data Mining: A Literature Review [Internet]. Available from: www.educationaldatamining.org.
  2. Liu Y, Li Z, Xiong H, Gao X, Wu J. Understanding of internal clustering validation measures. In: Proceedings - IEEE International Conference on Data Mining, ICDM. 2010. pp. 911–6.
  3. Mamdouh Fouad M, Yu PS, Fattah Mashat A, Fouad MM, Arabia Tarek Gharib SF. Efficient Clustering Technique for University Admission Data [Internet]. Vol. 45, International Journal of Computer Applications. 2012. Available from: https://www.researchgate.net/publication/235333407
  4. Bogarín A, Romero C, Cerezo R, Sánchez-Santillán M. Clustering for improving Educational process mining. In: ACM International Conference Proceeding Series. Association for Computing Machinery; 2014. pp. 11–5.
  5. Dutt A, Ismail MA, Herawan T. A Systematic Review on Educational Data Mining. Vol. 5, IEEE Access. Institute of Electrical and Electronics Engineers Inc.; 2017. pp. 15991–6005.
  6. Harwati, Alfiani AP, Wulandari FA. Mapping Student’s Performance Based on Data Mining Approach (A Case Study). Agriculture and Agricultural Science Procedia. 2015; 3:173–7.
  7. Campagni R, Merlini D, Sprugnoli R, Verri MC. Data mining models for student careers. Expert Systems with Applications. 2015 Aug 1; 42(13):5508–21.
  8. Liu Y, Li Z, Xiong H, Gao X, Wu J, Wu S. Understanding and enhancement of internal clustering validation measures. IEEE Transactions on Cybernetics. 2013 Jun; 43(3): 982–94.
  9. Ghorpade-Aher J, Metre VA. PSO based Multidimensional Data Clustering: A Survey. Vol. 87, International Journal of Computer Applications. 2014.
  10. López MI, Luna JM, Romero C, Ventura S. Classification via clustering for predicting final marks based on student participation in forums.
  11. El-Halees A, Tair MMA, El-Halees AM. Mining Educational Data to Improve Students’ Performance: A Case Study Cite this paper Mining Educational Data to Improve Students’ Performance: A Case Study. 2012; 2(2). Available from: http://www.esjournals.org.
  12. DeFreitas K, Bernard M. COMPARATIVE PERFORMANCE ANALYSIS OF CLUSTERING TECHNIQUES IN EDUCATIONAL DATA MINING. IADIS International Journal on Computer Science and Information Systems. 10(2): 65–78.
  13. Durairaj M, Vijitha C. Educational Data mining for Prediction of Student Performance Using Clustering Algorithms [Internet]. Available from: www.ijcsit.com.
  14. Peña-Ayala A. Educational data mining: A survey and a data mining-based analysis of recent works. Vol. 41, Expert Systems with Applications. 2014. pp. 1432–62.
  15. Romero C, Ventura S. Educational data science in massive open online courses. Vol. 7, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. Wiley-Blackwell; 2017.
  16. Romero C, Ventura S. Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 2020 May 1; 10(3).