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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 ]



 Daniel Rodic

Portrait photo




 Multi-Agent Systems
Data Mining


 Degree specific information: PhD


 Intelligent Distributed Agent Based Architecture, INDABA


This thesis presents work done on developing a multi-robot system architecture for cooperation. The thesis and the architecture presented herein focuses on two aspects of a multi-robot systems that form INDABA: Hybrid Agent Architecture and Framework for Cooperation. Hybrid Agent Architecture presented here combines the sub-symbolic knowledge representation layered architecture with a symbolic layer that allows for deliberative cooperation and social relationships. In this manner, the best characteristics of both approaches are utilised while their weaknesses are rectified by such a complementary approach. The Framework for Cooperation fully utilises a symbolic portion of the agents participating in the architecture in order to provide a framework for positive interaction - cooperation. The framework caters for heterogeneous agents with not necessarily a common set of beliefs, desires and intentions. From a topological view, the architecture presented in this thesis is a hybrid architecture. There is a central component of the whole system (centralised approach) but it is by no means the controlling component (decentralised approach). The central component of the system has more of a facilitating than controlling role.

 Supervisor / Co-Supervisor:

 AP Engelbrecht


 Not available for download yet.


 Degree specific information: M.Sc


 A Hybrid Heuristic Approach for Rule Extraction


The topic of this thesis is knowledge discovery algorithms. The knowledge discovery process and associated problems are discussed, followed by an overview of three classes of artificial intelligence based knowledge discovery algorithms. Typical representatives of each of these classes are presented and discussed in greater detail. Then a new knowledge discovery algorithm, called Hybrid Classifier System (HCS), is presented. The guiding concept behind the new algorithm was simplicity. The new knowledge discovery algorithm is loosely based on schemata theory. It is evaluated against CN2, C4.5, BRAINNE and BGP. Results are discussed and compared. A comparison was done using a benchmark of classification problems. These results show that the new knowledge discovery algorithm performs satisfactory, yielding accurate, crisp rule sets. Probably the main strength of the HCS algorithm is its simplicity, so it can be the foundation for many possible future extensions.

 Supervisor / Co-Supervisor:

 AP Engelbrecht


 Not available for download yet.

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