Using Color Coded Pattern Clustering Model Combined with Automated System for Courses Scheduling

Abstract

Course scheduling is very important but complex task at universities. The complexity increases when a course includes lab hours in addition to the lecture hours. Course schedule often yields to dissatisfaction of students, instructors, and university administration. In this paper, I discuss a Color Coded Pattern Clustering Model that applies a reasoning approach to design the time table in the Computer Science Department at University of Bahrain. The model supports minimizing clashes between lectures and lab sessions and facilitate distributing the courses among the time blocks. The model, then has been implemented to automatically generate the time-table and support data mining. It supports minimizing clashes in students time table and preserves faculty preferences.