Lecture Timetable System’s Data Mining Case Study: SUST Timetable System

Abstract

There are many computerized systems developed and used for many years in Sudan University of Science and Technology (SUST). These systems stored huge amount of data in auxiliary storage. In this research, the data from both SUST Timetable System specially Arabic and English lectures and SUST Registration System of academic years from 2009/2010 - 2011/2012 are used in mining, where the twostep algorithm is used to cluster the data as descriptive mining technique. SPSS Clementine is used to build the model, and Microsoft SQLServer is used to store the data set. The process of data mining led to the discovery of interesting information that may help in having better timetable or can be used for another purposes. This discovered information focuses about teachers, classrooms and their capacity, location of classrooms verses location of student’s college, location of the college of teacher ve rses location of student’s college, and other information.