Using Growing Hierarchical Self-Organizing Maps for Document Classification

                   Michael Dittenbach, Dieter Merkl, Andreas Rauber
          Institute of Software Technology, Vienna University of Technology
                   Favoritenstr. 9-11 / 188, A-1040 Vienna, Austria
                        {mbach, dieter, andi}@ifs.tuwien.ac.at
                        
                        
Abstract:
The self-organizing map has shown to be a stable neural network model for
high-dimensional data analysis. However, its applicability is limited by 
the fact that some knowledge about the data is required to define the size 
of the network.
In this paper we present the Growing Hierarchical SOM.This dynamically 
growing architecture evolves into a hierarchical structure of self-organizing 
maps according to the characteristics of the input data. Furthermore, each map 
is expanded until it represents the correspondig subset of the data at a 
specific level of granularity. We demonstrate the benefits of this novel 
model using a real world example from the text classification domain.

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M. Dittenbach, D. Merkl and A. Rauber: {\bf
Using Growing Hierarchical Self-Organizing Maps for Document Classification\\
In: Proceedings of the 8. European Symposium on Artificial Neural Networks (ESANN'2000) April 26-28, 2000, Bruges, Belgium.