An Optimised Approach for Student's Academic Performance by K-Means Clustering Algorithm Using Weka Interface

Provided by: Institute of Research and Journals (IRAJ)
Topic: Big Data
Format: PDF
One of the significant facts in higher learning institution is the explosive growth educational database. These databases are rapidly increasing without any benefit to manage the database. The clustering techniques have a wide use and importance now-a-days and this importance tends to increase as the amount of data grows. In this paper, K-means clustering technique is applied to analyze student academic performance. This study makes use of cluster analysis to segment students into groups according to their characteristics. This include the students evaluation factors like class internal marks, GPA, mid and final exam, assignment and lab-work are studied.

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