Applied Intelligence, 18-3
With the increasing amount of textual information available in electronic<br> form, more powerful methods for exploring, searching, and organizing the<br> available mass of information are needed to cope with this situation.<br> This paper presents the SOMLIB digital library system, built on neural<br> networks to provide text mining capabilities. At its foundation we use the<br> self-organizing map to provide content-based clustering of documents. By<br> using an extended model, i.e. the growing hierarchical self-organizing map,<br> we can further detect subject hierarchies in a document collection, with<br> the neural network adapting its size and structure automatically during its<br> unsupervised training process to reflect the topical hierarchy.<br> By mining the weight vector structure of the trained maps our system is<br> able to select keywords describing the various topical clusters.<br> Text mining has to incorporate more than the mere analysis of content.<br> Structural and genre information are key in organizing and locating<br> information. Using color-coding techniques we can integrate a structural<br> analysis of documents based on self-organizing maps into the subject-based<br> clustering relying on metaphor graphics for intuitive visualization.<br> We demonstrate the capabilities of the SOMLib system using collections of <br> articles from various newspapers and magazines.<br> <br> Keywords: Document Clustering, Self-Organizing Map (SOM), Genre Analysis,<br> Metaphor Graphics, Digital Libraries.
Information and Communication Technology