Using Growing hierarchical self-organizing maps for document classification.
Michael Dittenbach, Dieter Merkl, Andreas Rauber · The European Symposium on Artificial Neural Networks · 2000
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 de ne 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 c haracteristicsof the input data. Furthermore, each map is expanded until it represents the correspondig subset of the data at a speci c level of granularity. We demonstrate the bene ts of this novel model using a real world example from the text classi cation domain.