Spherical Topology Self-Organizing Map Neuron Network for Visualization of Complex Data

Acknowledgements Firstly, thanks to my supervisor Tom Gedeon, and Dingyun Zhu for their recommendations and support on this project. Moreover, thanks to Uwe R. Zimmer for his suggestions about the technique of report writing. Finally, thanks to my family and my friends for their encouragements. Abstract The spherical SOM (SSOM) has been proposed in order to remove the " border effect " in conventional Self-Organizing Maps (SOM). However, SSOM still has limitations in representing a sequence of events. The concentric spherical Self-Organizing Maps (CSSOM) is proposed in this report, because it can use an arbitrary number of spheres and that topology could be applied in analysis of sequential and time series data. I present a new method to extend SSOM and to reconstruct the neighbors in order to implement concentric spherical Self-Organizing Maps. Moreover, for ease of evaluation, I present the display schemas and several measurements for the quality of SOMs. I present the experimental results. The results indicate that the quality of SOM is improved through using specified CSSOM depending on the characteristics of the dataset. However, the results for sequence training as currently proposed needs improvement. Finally, the quality of clustering becomes worse, as the number of spheres increases and the number of units in each sphere decreases.