Understanding high-dimensional real-world data usually requires learning the structure of the data space. The structure may contain high-dimensional clusters that have important topological relationships. Methods such as merge clustering and self-organizing maps are designed to aid the visualization of such data. However, these methods often fail to capture critical structural properties of the input. Although self-organizing maps capture high-dimensional topology, they do not represent cluster boundaries or discontinuities. Merge clustering extracts clusters, but it does not capture local or global topology. This thesis presents an algorithm that combines the topology-preserving characteristics of self-organizing maps with a flexible, adaptive structure that learns cluster boundaries in the data. It also proposes a method for analyzing the quality of such visualizations, and outlines how it could be used for automatic parameter tuning.

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Masters Thesis, Department of Computer Sciences, The University of Texas at Austin. Technical Report AI95-238.

@mastersthesis{blackmore:masters95, title={Visualizing High-Dimensional Structure with the Incremental Grid Growing Network}, author={Justine Blackmore}, school={Department of Computer Sciences, The University of Texas at Austin}, address={Austin, TX}, note={Technical Report AI95-238}, url="http://www.cs.utexas.edu/users/ai-lab?blackmore:masters95", year={1995} }