The Growing Hierarchical Self-Organizing Map:
       Exploratory Analysis of High-Dimensional Data

    Andreas Rauber, Dieter Merkl, and Michael Dittenbach
  Department of Software Technology and Interactive Systems
           Vienna University of Technology
    Favoritenstr. 9-11 / 188, A-1040 Vienna, Austria
     e-mail: {andi, dieter, mbach}@ifs.tuwien.ac.at


Abstract:

The self-organizing map is a very popular unsupervised neural network
model for the analysis of high-dimensional input data as in data mining
applications. However, 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 presented in this 
paper we address both limitations.
The growing hierarchical som is an artificial neural network model with 
hierarchical architecture composed of independent growing self-organizing 
maps.
The motivation was to provide a model that adapts its architecture during 
its unsupervised training process according to the particular requirements 
of the input data.
Furthermore, by providing a global orientation of the independently growing 
maps in the individual layers of the hierarchy, navigation across branches 
is facilitated.
The benefits of this novel neural network are first, a problem-dependent 
architecture, and second, the intuitive representation of hierarchical 
relations in the data. This is especially appealing in explorative data 
mining applications, allowing the inherent structure of the data to unfold 
in a highly intuitive fashion.
 
Keywords: Self-Organizing Map (SOM), Data Mining, Hierarchical 
Clustering, Exploratory Data Analysis, Pattern Recognition.

------------------------------
In: IEEE Transactions on Neural Networks, 
Vol 13, No 6, pp. 1331-1341, November 2002, IEEE