Students Academic Performance Using Partitioning Clustering Algorithms

Humamuddin , Md. Tabrez Nafis, Syed Taha Owais


With time, the data is growing at a very high rate. The issue is not in storing the data, but in extracting the valuable information from it. Data mining techniques serve as a good means for extracting valuable patterns (knowledge) from the data. Now, talking about the Educational Field, Academic Institutes and Universities are worried about their student’s performance because it’s a key factor for the growth and ranking of the institutes. Data in educational institutes is also growing at a very high rate as the number of students are increasing every year. It’s a tedious task to monitor and predict the performance of students by normal methods. Data Mining techniques are very helpful in doing this job. In this paper, we have used K-Means, K-Medoids and X-Means clustering algorithms which will help in categorizing the students into several groups based on their performance.


Data Mining, Clustering, Student performance, K-means, K-Medoids, X-Means

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