Hierarchical Clustering of Document Archives with the Growing Hierarchical Self-Organizing Map

With the increasing amount of information available in electronic document collections, methods for organizing these collections to allowtopic-orien ted browsing and orientation gain increasing importance. In this paper, we present the Growing Hierarchical Self-Organizing Map, which allows an automatic hierarchical decomposition and organization of documents. We present a case study based on a 3-month article collection from an Austrian daily newspaper.

[1]  Andreas Rauber,et al.  The SOMLib Digital Library System , 1999, ECDL.

[2]  Risto Miikkulainen,et al.  Script Recognition with Hierarchical Feature Maps , 1992 .

[3]  Andreas Rauber,et al.  The growing hierarchical self-organizing map , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[4]  Pasi Koikkalainen,et al.  Self-organizing hierarchical feature maps , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[5]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[6]  T. Kohonen.  Self-organized formation of topology correct feature maps , 1982 .

[7]  Risto Miikkulainen,et al.  Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map , 1993, IEEE International Conference on Neural Networks.

[8]  Andreas Rauber,et al.  Creating an Order in Distributed Digital Libraries by Integrating Independent Self-Organizing Maps , 1998 .

[9]  Risto Mukkulainen,et al.  Script Recognition with Hierarchical Feature Maps , 1990 .

[10]  A. Rauber,et al.  Automatic Labeling of Self-Organizing Maps forInformation RetrievalDieter Merkl , 1999 .

[11]  Bala Srinivasan,et al.  Dynamic self-organizing maps with controlled growth for knowledge discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..