Improving the quality of labels for self-organising maps using fine-tuning
Erich Schweighofer, Andreas Rauber, Michael Dittenbach · 2002
Vector representation of legal documents is still the best way for computing classification clusters and labelling of its contents. A very special problem occurs with self organising maps: strong clusters tend to dominate neighbouring smaller clusters in terms of their weight vector structure, which influences the labels extracted from these. This unwelcome side-effect can be overcome efficiently with a dedicated fine-tuning phase at the end of the training process, in which the neighbourhood radius of the training function is set to zero. Experiments with our text collection show the great improvement of the quality of labelling.