Abstract:
This study aims to use modern Data Mining techniques to analyze admission data of Sudanese universities. The current methodology contains two data warehouse structures; a star and a snowflake. A star data warehouse structure was used to develop several Association Rules Mining models. The snowflake data warehouse structure was used to create a smart user interface based on OLAP (On Line Analytical Processing) technique as the first system for OLAP in Sudan. However the Association Rules Mining technique is often used in the business intelligence field, and has limited applications in other fields. We could apply it in higher education field (s) to investigate relationships between attributes, if we developed several Association Rules Mining models. To improve the efficiency and effectiveness of data analysis tasks, the accuracy of those mining models was compared. Through our developed user interface based on OLAP system, end users could easily find the answer of their queries in seconds about data regards to different dimensions. Through this study we supported that Data Mining techniques have huge potential benefits in terms of multidimensional analysis and can help to solve Sudan’s education need for skilled analysts. We could discover some hidden patterns what have been done and what is the result is not clear. Moreover, we could solve our typical research questions, such as: What percent of faculty choices do students usually choose on the application form? Is there a need for all these number of faculty choices offered to students on their application forms? Are there any associations between the variation of students’ faculty preferences and students’ geographical locations, in residential provinces?
