In his findings, Wakabi proposes genetic algorithmsÃ¢‚¬based techniques aimed at narrowing the existing gaps in the association rule mining arena including algorithmic complexity and scaling methods in ARM environments. He suggests a new algorithm to generate association rules using Ã¯¬ve rule quality metrics (comprehensibility, conÃ¯¬dence, J-measure, surprise and lift). This combination of rule quality metrics permits a user to evaluate the association rules on the diÃ¯¬‚¬erent metrics in a single algorithm run.
Chairperson - Dr. Josephine Nabukenya Ã¢‚¬ Dean,Ã‚ School of Computing and Informatics Technology (CIT)
Dr. Florence Tushabe Ã¢‚¬ Chair, Department of Computer Science (CIT)
Dr. John Quinn Ã¢‚¬ Senior Lecturer, Department of Computer Science
Dr. John Ngubiri Ã¢‚¬ Senior Lecturer and Deputy Principal, CoCIS
Dr. J. Mango
Prof. Venansius Baryamureeba
Prof. Karunakaran Sarukesi