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Student Performance Evaluation in Education Sector Using Prediction and Clustering Algorithms


Rupnawar Sachin Hanumant , SBPCOE,Indapur; Solankar Punam Anil, SBPCOE,Indapur; Jagatap Trupti Baban, SBPCOE,Indapur; Shitole Vibhavari Jayvant, SBPCOE,Indapur; Prof. Kumbhar S. L., SBPCOE,Indapur


Data Mining, Clustering, Classification, Predictive Model


Data mining is the crucial steps to find out previously unknown information from large relational database. various technique and algorithm are their used in data mining such as association rules, clustering and classification and prediction techniques. Ease of the techniques contains particular characteristics and behaviour. In this paper the prime focus on clustering technique and prediction technique. Now a days large amount of data stored in educational database increasing rapidly. The database for particular set of student was collected. The clustering and prediction is made on some detailed manner and the results were produce. The K-means clustering algorithm is used here. To find nearest possible a cluster a similar group the turning point India is the performance in higher education for all students. This academic performance is influenced by various factor, therefore to identify the difference between high learners and slow learner students it is important for student performance to develop predictive data mining model.

Other Details

Paper ID: IJSRDV3I100139
Published in: Volume : 3, Issue : 10
Publication Date: 01/01/2016
Page(s): 229-231

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