Application of self organizing map for knowledge discovery based in higher education data

• Main Authors: Sim, Alex Tze Hiang, Saadatdoost, Robab, Jafarkarimi, Hosein
• Format: Conference or Workshop Item
• Published: 2011
• Online Access: http://eprints.utm.my/id/eprint/45604/; http://dx.doi.org/10.1109/ICRIIS.2011.6125693

This paper focuses on knowledge discovery among attributes of Iran Higher Education Institute using self organizing map (SOM); the key problem with massive volume of data is extracting knowledge and patterns that are hidden in data. Managerial needs to explore this data for the purpose of decision making and strategy making reveals its importance. Furthermore it can be useful for researchers that study and research about higher education. Meanwhile planning for higher education has significant impact on developing of one society, successful planning needs to analysis some huge and historical data that is available in higher education institutes. SOM is a particular type of neural network used in clustering and helps discover patterns and relations without advanced knowledge about them. The steps of this approach can be discussed under five headings, which are (i) Data Preparation (ii) Data Loading, (iii) Initializing, (iv) Map training and (v) Interpretation of the results. The target dataset contains data of five universities located in Tehran, Iran affiliated to Medical Ministry of Iran and the most important attributes are program of study, learning style, study mode and degree. Results show that the number of enrolling students for Tehran medical university has decreased for the past 23 years from 1988 to 2005. This study also finds that Tehran University of Medical Science covers the majority of high degrees like MDdisplay(Doctor of Medicine) and PhD. The findings of this study can be used in improving of higher education decision making systems and the results of this study indicate SOM toolbox utility in similar institutes to knowledge discovery in a visualizing way.