Data Mining: Classification Techniques of Students’ Database A Case Study of the Nile Valley University, North Sudan

International Journal of Computer Trends and Technology (IJCTT)
© 2014 by IJCTT Journal
Volume-16 Number-5 
Year of Publication : 2014
Authors : Tariq O. Fadl Elsid , Mirghani. A. Eltahir
DOI :  10.14445/22312803/IJCTT-V16P146


Tariq O. Fadl Elsid , Mirghani. A. Eltahir. "Data Mining: Classification Techniques of Students’ Database A Case Study of the Nile Valley University, North Sudan". International Journal of Computer Trends and Technology (IJCTT) V16(5):192-203, Oct 2014. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
The growth of internet is increasing rapidly and the use of systems become very common. A common main problem that faces any system administration or any users is data increasing per-second, which is stored in different type and format in the servers, learning about students from a huge amount of data including personal details, registration details, evaluation assessment, performance profiles, and many more for students and lecturers alike. Graduation and academic information in the future and maintaining structure and content of the courses according to their previous results become importance. The paper objectives are extract knowledge from incomplete data structure and what the suitable method or technique of data mining to extract knowledge from a huge amount of data about students to help the administration using technology to make a quick decision. Data mining aims to discover useful information or knowledge by using one of data mining techniques, this paper used classification technique to discover knowledge from student’s server database, where all students’ information were registered and stored. The classification task is used, the classifier tree C4.5, to predict the final academic results, grades, of students. We use classifier tree C4.5 as the method to classify the grades for the students .The data include four years period [2006-2009]. Experiment results show that classification process succeeded in training set. Thus, the predicted instances is similar to the training set, this proves the suggested classification model. Also the efficiency and effectiveness of C4.5 algorithm in predicting the academic results, grades, classification is very good. The model also can improve the efficiency of the academic results retrieving and evidently promote retrieval precision.

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Data Mining, Classification, Knowledge Discovery in Database(KDD), J48 Algorithm, Weka