Enhancement of topology preservation and hierarchical dynamic self-organising maps for data visualisation
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 GSOM, therefore the authors propose an algorithm to enhance topographic quality of GSOM by means of recursive mean directed growing (RMDG) in the growing phase of GSOM while maintaining or even improving its quantisation quality. Furthermore, the authors introduce a dynamic SOM tree model, or hierarchical GSOM, to identify clusters with better accuracy and to visualise cluster separation and merging. Results show improvement of topography preservation when compared to GSOM, and SOM that has similar map size but is not of topologically optimum map aspect ratio. The dynamic SOM tree model demonstrates the ability to allow users to identify clusters interactively and at the same time understand how a larger cluster breaks up into smaller clusters (if it has any) and/or smaller clusters group to form a larger cluster. (C) 2002 Elsevier Science Inc. All rights reserved.
topology preservation self-organising maps clustering data mining visualisation tools hierarchical som