September 10, 2025Deleted Journal

Optimising Educational Performance through Clustering Algorithm Evaluation

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This analysis compares clustering techniques to assess student performance, highlighting implications for educational data mining.

Clustering methodologies successfully identify and categorize students based on their academic performance metrics, enhancing targeted interventions.
K-Means, DBSCAN, BIRCH, and Expectation Maximization were evaluated for their effectiveness in classifying students' educational achievements.
The study reveals significant advantages and limitations in various clustering approaches aiding educators to optimize performance outcomes.
Adoption of sophisticated data mining techniques is essential to guide academic institutions in improving student support strategies.

Amartya Ghosh (2025) studied this question.

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