Automatic labeling of self-organizing maps for information retrieval

The self-organizing map is a very popular unsupervised neural network model for the analysis of high-dimensional input data as in information retrieval applications. However, the interpretation of the map requires much manual effort, especially as far as the analysis of the learned features and the characteristics of identified clusters is concerned. We present our novel LabelSOM method which, based on the features learned by the map, automatically selects the most descriptive features of the input patterns mapped onto a particular unit of the map, thus making the characteristics of the various clusters within the map explicit. We demonstrate the benefits of this approach on an example from text classification using a real-world document archive. In this particular case, the features correspond to keywords describing the contents of a document. The benefit of this approach is that the various document clusters are characterized in terms of shared keywords, thus making it easy for the user to explore the contents of an unknown document archive.