Document classification with self-organizing maps

The self-organizing map is a very popular unsupervised neural network for the analysis of high-dimensional input data as in information retrieval applications. The reason is that the map display provides a convenient possibility for the user to explore the contents of a document archive. From geography, however, it is known that maps are not always the best way to represent information spaces. Often it is better to provide a hierarchical view of the underlying data collection in form of an atlas where, starting from a map representing the complete data collection, different regions are shown at finer levels of granularity. Using an atlas, the user can easily "zoom" into regions of particular interest while still having general maps for overall orientation. We show that a similar display can be obtained by using hierarchical feature maps to represent the contents of a document archive. These neural networks have a layered architecture where each layer consists of a number of individual self-organizing maps. By this, the contents of the document archive may be represented at arbitrary detail while still having the general maps available for global orientation.