Publications in Scientific Journals:
M. Dittenbach, A. Rauber, W. Merkl:
"Uncovering Hierarchical Structure in Data Using the Growing Hierarchical Self-Organizing Map";
Discovering the inherent structure in data has become one of the major
challenges in data mining applications. It requires stable and adaptive
models that are capable of handling the typically very high-dimensional
feature spaces. In particular, the representation of hierarchical relations
and intuitively visible cluster boundaries are essential for a wide range
of data mining applications. Current approaches based on neural networks
hardly fulfill these requirements within a single model.
In this paper we present the Growing Hierarchical Self-Organizing Map
(GHSOM), a neural network model based on the self-organizing map. The main
feature of this novel architecture is its capability of growing both in
terms of map size as well as in a three-dimensional tree-structure in order
to represent the hierarchical structure present in a data collection during
an unsupervised training process. This capability, combined with the
stability of the self-organizing map for high-dimensional feature space
representation, makes it an ideal tool for data analysis and exploration.
We demonstrate the potential of the GHSOM with an application from the
information retrieval domain, which is prototypical both of the
high-dimensional feature spaces frequently encountered in today's
applications as well as of the hierarchical nature of data.
Keywords: self-organizing map (SOM), unsupervised hierarchical clustering,
document classification, data mining, exploratory data analysis
Electronic version of the publication:
Created from the Publication Database of the Vienna University of Technology.