Abstract
Educational Data Mining is an emerging field in the data mining domain. In this competitive world scenario, the quality of education needs to improve. Unfortunately most of the students’ data are becoming data tombs for not analyzing the hidden knowledge. The educational data mining tries to uncover the hidden knowledge by discovering relationships between student learning characteristics and behavior. With this educational data modeling, the educators may plan for future learning pedagogy to support the student’s learning style. This knowledge may be applied by the academic planners to improve the quality of education and decrease the failure rate. In this paper, we had collected real dataset containing 666 instances with 11 attributes. The data is from the Common Entrance Examination (CEE) data of a particular year for admission to medical colleges of Assam, India conducted by Dibrugarh University. We tried to find out the association rules using the data. Various clustering and classification methods were also used to compare the suitable one for the dataset. The data mining tools applied in the educational data were Orange, Weka and R Studio.
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Hussain, S., Atallah, R., Kamsin, A., Hazarika, J. (2019). Classification, Clustering and Association Rule Mining in Educational Datasets Using Data Mining Tools: A Case Study. In: Silhavy, R. (eds) Cybernetics and Algorithms in Intelligent Systems . CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 765. Springer, Cham. https://doi.org/10.1007/978-3-319-91192-2_21
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