Knowledge Discovery to Analyze Student Performance using k-mean Clustering depend upon various mean values input methods: A Case Study
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Abstract
The main objective of educational institutions is to provide high quality of education. Providing a high quality of education depends on predicting the unmotivated students before they entering in to final examination. One way to achieve quality to higher education system is by discovering knowledge of student in particular subject. Data Clustering is used to extract meaningful information and plays a vital role in data mining. Its main job is to group the similar data together based on the characteristic they possess. The mean values are the centroids of the specified number of cluster groups. The centroids, though gets changed during the process of clustering, are calculated using several methods. In this paper, the k-mean clustering algorithm, depending upon various mean values input methods is used to discover knowledge that describes student performance. This study will help the teachers to reduce the drop out ratio, improve the performance of students and help identifying students who need special attention.
Keywords: Data Mining, Knowledge discovery, Cluster techniques, K-mean Method.
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