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OVERVIEW

The focus of the neural networks group is to investigate aspects of training and optimization of neural networks, and to apply neural networks to solve real-world problems. The activities of this focus area are mainly centered around architecture selection, active learning, and the development of new an efficient training algorithms. Some work is done on self-organizing maps.

Current applications are directed towards data mining, spam detection, user authentication, fraud detection, gesture recognition, and trading on financial markets. Applications of self-organization maps to exploratory data analysis, data mining, and species identification are done.

ACTIVE MEMBERS

N Asaberere

PhD Started in 2011

A Wilford

M.Sc Started in 2010

A van Wyk

M.Sc Started in 2009
Hons-B.Sc Completed in 2008

T Museba

PhD Started in 2008

M van der Merwe

M.Sc Started in 2007

A Rakitianskaia

M.Sc Started in 2007

C Naicker

PhD Started in 2007
M.Sc Completed in 2006
Hons-B.Sc Completed in 2002

W van Heerden

M.Sc Started in 2002

ALUMNI MEMBERS

List alumni of this research focus area. [ Show ]

GROUP PUBLICATIONS

List publications of this research focus area. [ Show ]

MEMBER PROFILE



 Name:

 Willem van Heerden

Portrait photo

 E-mail:

 wvheerden@cs.up.ac.za

 Group(s):

 Data mining
Neural Networks

 

 Degree specific information: M.Sc

 Title:

 Exploratory Data Analysis and Data Mining with Self-Organising Feature Maps

 Abstract:

The Self-Organising Feature Maps (SOM) is an unsupervised machine learning approach that offers extremely useful data clustering and visualisation abilities. The approach has been successfully employed to a wide variety of problems. Exploratory Data Analysis (EDA) and Data Mining (DM) are both fields that attempt to algorithmically extract insightful knowledge from a data set, with a greater or lesser level of human assistance. This thesis aims to provide a complete survey of EDA and DM approaches that specifically utilise a SOM implementation. A new, hybrid approach to DM rule extraction, using a SOM in conjunction with other DM algorithms is presented. The hybrid approach is compared experimentally to existing approaches. Empirical results are presented to illustrate the relative merits of each EDA and DM approach.

 Supervisor / Co-Supervisor:

 AP Engelbrecht

 Thesis:

 Not available for download yet.




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