Contextual Maps for Browsing Huge Document Collections

  • Krzysztof Ciesielski
  • Mieczysław A. Kłopotek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


The increasing number of documents returned by search engines for typical requests makes it necessary to look for new methods of representation of contents of the results, like document maps. Though visually impressive, doc maps (e.g. WebSOM) are extensively resource consuming and hard to use for huge collections.

In this paper, we present a novel approach, which does not require creation of a complex, global map-based model for the whole document collection. Instead, a hierarchy of topic-sensitive maps is created. We argue that such approach is not only much less complex in terms of processing time and memory requirement, but also leads to a robust map-based browsing of the document collection.


Document Collection Contextual Model Normalize Mutual Information Average Path Length Document Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Boulis, C., Ostendorf, M.: Combining multiple clustering systems. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Proceeding of the International Conference on Artificial Neural Networks 1997, pp. 613–618. Springer, Heidelberg (1997)Google Scholar
  3. 3.
    Halkidi, M., Batistakis, Y., Vazirgiannis, M.: On clustering validation techniques. Journal of Intelligent Information Systems 17(2-3), 107–145 (2001)MATHCrossRefGoogle Scholar
  4. 4.
    Hung, C., Wermter, S.: A constructive and hierarchical self-organising model in a non-stationary environment. In: International Joint Conf. on Neural Networks (2005)Google Scholar
  5. 5.
    Klopotek, M., Wierzchon, S., Ciesielski, K., Draminski, M., Czerski, D.: Conceptual maps and intelligent navigation in document space (in Polish). Akademicka Oficyna Wydawnicza EXIT Publishing, Warszawa (to appear, 2006)Google Scholar
  6. 6.
    Kohonen, T., Kaski, S., Somervuo, P., Lagus, K., Oja, M., Paatero, V.: Self-organization of very large document collections, Helsinki University of Technology technical report (2003),
  7. 7.
    Zhang, T., Ramakrishan, R., Livny, M.: BIRCH: Efficient data clustering method for large databases. In: Proceedings of ACM SIGMOD International Conference on Data Management (1997)Google Scholar
  8. 8.
    Zhao, Y., Karypis, G.: Criterion functions for document clustering: Experiments and analysis,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Krzysztof Ciesielski
    • 1
  • Mieczysław A. Kłopotek
    • 1
    • 2
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarszawaPoland
  2. 2.Institute of Computer ScienceUniversity of PodlasieSiedlcePoland

Personalised recommendations