Growing Hierarchical Self-Organizing Map (GHSOM)

The Growing Hierarchical Self-Organizing Map (GHSOM) is a neural network architecture based on the Self-Organizing Map (SOM). It organizes high-dimensional data according to the inherent structure by dynamically creating a hierarchy of growing SOMs. For the most recent version of the software visit the Java SOMToolbox page (http://www NULL.ifs NULL.tuwien NULL.html) of the Data Mining group at the Institute of Software Technology and Interactive Systems, Vienna University of Technology.

PlaySOM and PocketSOMPlayer

With the rising popularity of digital music archives the need for new access methods such as interactive exploration or similarity-based search become significant. We have developed PlaySOM, as well as the PocketSOMPlayer, two novel interfaces that allow for browsing a music collection by navigating through a map of clustered music tracks and to select regions of interest containing similar tracks for playing. The PlaySOM system is primarily designed to allow interaction via a large-screen device, whereas the PocketSOMPlayer is implemented for mobile devices, supporting both local as well as streamed audio replay. This approach offers content-based organization of music as an alternative to conventional navigation of audio archives, i.e. flat or hierarchical listings of music tracks that are sorted and filtered by meta information.

SOM Visualization

Self-Organizing Maps have been applied in various industrial applications and have proven to be a valuable data mining tool. In order to fully benefit from their potential, advanced visualization techniques assist the user in analyzing and interpreting the maps. We have developed new methods for depicting the SOM based on vector fields, namely the Gradient Field and Borderline visualization techniques, to show the clustering structure at various levels of detail. This method can be used on aggregated parts of the SOM that show which factors contribute to the clustering structure and for finding correlations and dependencies in the underlying data.


KONTERM is a research project of the Research Center for Computers and Law, Institute of Public International Law, University of Vienna, Faculty of Law in cooperation with the Institute of Applied Informatics, Department for Informations Systems, University of Vienna, and the Institute of Software Engineering, Technical University of Vienna. It aims at the semiautomatic analysis of legal documents. The SOM and GHSOM neural networks are used to provide content-based organization of legal documents.


If you are interested in commercial search and text analytics solutions and consulting, send me an e-mail (m NULL.dittenbach null@null max-recall or call the max-recall (http://www NULL.max-recall phone number: +43 720 978603. I am also happy to answer e-mails regarding my research topics.

Location: Vienna, Austria