HomeNews&EventsCIT NewsPeter Patrick Wakabi-Waiswa defended his PhD titled: Association Rule Mining Using Evolutionary Computing
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Peter Patrick Wakabi-Waiswa defended his PhD titled: Association Rule Mining Using Evolutionary Computing

In his findings, Wakabi proposes genetic algorithms€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 five rule quality metrics (comprehensibility, confidence, J-measure, surprise and lift). This combination of rule quality metrics permits a user to evaluate the association rules on the difl¬erent metrics in a single algorithm run.

Committee

Chairperson - Dr. Josephine Nabukenya € Dean, School of Computing and Informatics Technology (CIT)

Internal Examiners

Dr. Florence Tushabe € Chair, Department of Computer Science (CIT)

Dr. John Quinn € Senior Lecturer, Department of Computer Science

Panelists

Dr. John Ngubiri € Senior Lecturer and Deputy Principal, CoCIS

Dr. J. Mango

Thesis Supervisors

Prof. Venansius Baryamureeba

Prof. Karunakaran Sarukesi

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Last modified on Tuesday, 23 July 2013 15:01
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