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M. Dittenbach, W. Merkl, A. Rauber:
"Organizing and Exploring High-Dimensional Data with the Growing Hierarchical Self-Organizing Map";
in: "Proceedings of the 1st International Conference on Fuzzy Systems and Knowledge Discovery (FSKD02)", Nanyang Technical University Singapore, Singapore, 2002, ISBN: 9810475365.

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 caused, on
the one hand, by the static architecture of this model, as well as, on the
other hand, by the limited capabilities for the representation of
hierarchical relations of the data.
With our growing hierarchical self-organizing map we present an artificial
neural network model with hierarchical architecture composed of
independent growing self-organizing maps to address both limitations.
The motivation is to provide a model that adapts its architecture during
its unsupervised training process according to the particular requirements
of the input data. The benefits of this neural network are first, a
problem-dependent architecture, and second, the intuitive representation
of hierarchical relations in the data. This is especially appealing in
exploratory data mining applications, allowing the inherent structure of
the data to unfold in a highly intuitive fashion.