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A. Rauber, W. Merkl, M. Dittenbach:
"The Growing Hierarchical Self-Organizing Map: Exploratory Data Analysis of High-Dimensional Data";
IEEE Transactions on Neural Networks, 13 (2002), 6; S. 1331 - 1341.

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.

http://ieeexplore.ieee.org/iel5/72/22620/01058070.pdf?isNumber=22620&prod=IEEE