Enhancement of topology preservation and hierarchical dynamic self-organising maps for data visualisation

Abstract

The use of self-organising maps (SOM) in unsupervised knowledge discovery has been successful and widely accepted, since the results produced are unbiased and can be visualised. Growing SOM (GSOM), or dynamic SOM that dynamically allocates map size and shape, was proposed to compensate for the static nature of Kohonen's SOM. GSOM has proven in experiments to decrease the time required to produce a feature map that is of appropriate size for the given data. However, although GSOM usually arrives at similar quantisation error when compared to SOM, it produces considerably higher topographic error. This property has significant influence on the quality of data visualisation and clustering using..