With the advance and massive growth of electronic text archives, the need for tools emerges, which
help to gain insight into the basic structure of the underlying digital library. We present a neural network approach for the analysis and exploration of text archives aiming at the detection and visualization of the inherent structure of the text collection. This cluster visualization technique called
Adaptive Coordinates is based on an extended learning rule for the self-organizing map. It provides an intuitive visualization by mapping clusters in a high-dimensional input space onto groups of nodes in a 2-dimensional output space. We further compare the results of this mapping with another prominent cluster visualization technique, namely
Sammon's Mapping.
