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CIRG - Research  -  Data Mining 



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OVERVIEW

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.

ACTIVE MEMBERS

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

ALUMNI MEMBERS

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

GROUP PUBLICATIONS

List publications of this research focus area. [ Show ]

MEMBER PROFILE



 Name:

 Eileen Dean

Portrait photo

 E-mail:

 deane@dit.ac.za

 Group(s):

 Data Mining

 

 Degree specific information: M.Sc

 Title:

 Artifical Intelligence Approach to Multi-Attribute Pattern Recognition for Tree Identification

 Abstract:

The aim of this thesis is to investigate Self-Organizing Maps (SOM) with a view to overcoming the problems of identification of biological tree specimens. The problems of identification of any biological material arise because of the variability exhibited by the phenotypes of any living organism. The knowledge obtained from domain experts on identified trees will be used to train a SOM neural network to discern amongst the different tree species (accomplished by performing a clustering process which groups together trees with similar characteristics). Given the available information on a speciment to be identified (often consisting of sparse data), the SOM will match the cluster formed from the unknown specimen with the closest cluster of the known tree samples and assign a probability. The success of the SOM neural Network to identify selected specimen will be tested and analysed.

 Supervisor / Co-Supervisor:

 AP Engelbrecht

 Thesis:

 Not available for download yet.




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