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Knowledge Discovery for Large Databases in Education Institutes
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Author(s): Robab Saadatdoost (Islamic Azad University, Parand Branch, Iran), Alex Tze Hiang Sim (Universiti Teknologi Malaysia, Malaysia), Hosein Jafarkarimi (Islamic Azad University, Damavand Branch, Iran)and Jee Mei Hee (Universiti Teknologi Malaysia, Malaysia)
Copyright: 2018
Pages: 88
Source title:
Information Retrieval and Management: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-5225-5191-1.ch010
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Abstract
This project presents the patterns and relations between attributes of Iran Higher Education data gained from the use of data mining techniques to discover knowledge and use them in decision making system of IHE. Large dataset of IHE is difficult to analysis and display, since they are significant for decision making in IHE. This study utilized the famous data mining software, Weka and SOM to mine and visualize IHE data. In order to discover worthwhile patterns, we used clustering techniques and visualized the results. The selected dataset includes data of five medical university of Tehran as a small data set and Ministry of Science - Research and Technology's universities as a larger data set. Knowledge discovery and visualization are necessary for analyzing of these datasets. Our analysis reveals some knowledge in higher education aspect related to program of study, degree in each program, learning style, study mode and other IHE attributes. This study helps to IHE to discover knowledge in a visualize way; our results can be focused more by experts in higher education field to assess and evaluate more.
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