Enhanced topology preservation of Dynamic Self-Organising Maps for data visualisation

Unsupervised knowledge discovery using Self Organising Maps (SOM) has been successfully used in obtaining unbiased and visualisable results. A Growing (or Dynamic) Self Organising Maps (GSOM) is an extended version of the original SOM with adaptive map size and controllable spread. In experiments a GSOM usually has considerably higher topographic error than SOM with similar quantisation error. This can be undesirable in cases where, topology preservation is important, therefore in this paper the authors proposed an algorithm to assist the growing of the dynamic self-organising map in achieving better topographic quality whilst maintaining or even improving level of quantisation error. Results have shown improvement of topographic error when comparing to GSOM, and have better topology preservation than non-topologically optimised SOM with similar map size