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The data and text mining focus area has the objective of developing new techniques for knowledge discovery and to improve existing techniques. The focus area is also active in applying data mining techniques to solve real-world problems in consultation to South African industries.

Some of the questions being addressed are how to mine knowledge from data with continuous classes, how to cope with extremely large databases, more efficient data clustering methods and how to extract knowledge in environments where data changes over time. Tools are currently under development which address these questions.


List the current members actively doing research in this focus area. [ Show ]


P Lutu

PhD Completed in 2010

A Louis

M.Sc Started in 2006

E Dean

M.Sc Started in 2002

G Nel

M.Sc Completed in 2005

E Papacostantis

M.Sc Started in 2004
Hons-B.Sc Completed in 2003

G Potgieter

M.Sc Completed in 2003
Hons-B.Sc Completed in 2001

D Rodic

PhD Completed in 2005
M.Sc Completed in 1999


List publications of this research focus area. [ Show ]



 Gert Nel

Portrait photo




 Evolutionary Computation
 Data Mining


 Degree specific information: M.Sc


 A Memetic Genetic Program for Knowledge Discovery


Local search algorithms have been proved to be effective in refining solutions that have been found by other algorithms. Evolutionary algorithms, in particular global search algorithms, have shown to be successful in producing approximate solutions for optimisation and classification problems in acceptable computation times. A relatively new method, memetic algorithms, uses local search to refine the approximate solutions produced by global search algorithms. This thesis develops such a memetic algorithm. The global search algorithm used as part of the new memetic algorithm is a genetic program that implements the building block hypothesis by building simplistic decision trees representing valid solutions, and gradually increases the complexity of the trees. The specific building block hypothesis implementation is known as the building block approach to genetic programming, BGP. The effectiveness and efficiency of the new memetic algorithm, which combines the BGP algorithm with a local search algorithm, is demonstrated.

 Supervisor / Co-Supervisor:

 AP Engelbrecht



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