In spite of the stability and popularity of the
Self-Organizing Map (SOM), at least two limitations have to be noted, which are related, on the one hand, to the static architecture of this model, as well as, on the other hand, to the limited capabilities for the representation of hierarchical relations of the data.
With our novel
Growing Hierarchical Self-Organizing Map (GHSOM) we address both limitations.
The growing hierarchical som is an artificial neural network model with
hierarchical architecture composed of independent growing self-organizing
maps.
By providing a global orientation of the independently growing
maps in the individual layers of the hierarchy, navigation across branches
is facilitated.
The
GHSOM is used as a basis for data organization in both the
SOMLib and
SOMeJB systems, and forms a core component in the
KONTERM project.
