In this project, we study approaches that combine visualization techniques for document collections with conventional keyword and content-based search methods. During the past period we focused on the development of interactive visualization techniques.
For the visualization of document collections and search results we developed models based on growing self-organizing maps. These maps can be used to arrange documents based on the similarity of the document's content . The trained and labeled map can then be used to visualize the structure of the underlying document collection as well as changes in the collection, e.g., insertion or removal of document subsets . Furthermore, visual information about document density, match of search hits to specific document groups, and similarity to a given sample document in content based searching can be given by different coloring methods. Besides for the analysis of text document collections, the developed models can also be applied for the analysis of multimedia data  or to post-process search engine result sets. The visualization and clustering of the obtained results provides additional visual information about the outcome of a search, which is more intuitive than a pure ordered list of search hits.
A further advantage of the developed approaches is that manually defined lists of index terms or a classification hierarchy, which are usually subjectively labeled and require expensive maintenance, are not needed. Especially, in rapidly changing document collections, such as collections of scientific research publications, classification systems that are not frequently updated and do not reflect the user’s classification criteria, are usually not accepted.