International Journal of Soft Computing

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Mining Student Data to Characterize Drop out Feature Using Clustering and Decision Tree Techniques

K. Shyamala and S.P. Rajagopalan
Page: 150-156 | Received 21 Sep 2022, Published online: 21 Sep 2022

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Abstract

Compared to traditional analytical studies that are often hindsight and aggregate, data mining is forward looking and is oriented to individual students. This study presents the work of data mining in predicting the drop out feature of students. This study applies decision tree technique to choose the best prediction and clustering analysis. The list of students who are predicted as likely to drop out from college by data mining is then turned over to teachers and management for direct or indirect intervention.

How to cite this article:

K. Shyamala and S.P. Rajagopalan . Mining Student Data to Characterize Drop out Feature Using Clustering and Decision Tree Techniques.
DOI: https://doi.org/10.36478/ijscomp.2007.150.156
URL: https://www.makhillpublications.co/view-article/1816-9503/ijscomp.2007.150.156