BEATCA: Map-Based Intelligent Navigation in WWW

  • Mieczysław A. Kłopotek
  • Krzysztof Ciesielski
  • Dariusz Czerski
  • Michał Dramiński
  • Sławomir T. Wierzchoń
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4183)


In our research work, we explore the possibility to exploit incremental, navigational maps to build visual search-and-recommendation system. Multiple clustering algorithms may reveal distinct aspects of the document collection, just pointing to various possible meanings, and hence offer the user the opportunity to choose his/her own most appropriate perspective. We hope that such a system would become an important step on the way to information personalization. The paper presents the architectural design of our system.


intelligent user interfaces visualization Web mining 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Berry, M.W., Drmac, Z., IJessup, E.R.: Matrices, vector spaces and information retrieval. SIAM Review 41(2), 335–362 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Chen, J., Sun, L., Zaiane, O.R., Goebel, R.: Visualizing and Discovering Web Navigational Patterns (2004),
  3. 3.
    Ciesielski, K., Draminski, M., Klopotek, M., Kujawiak, M., Wierzchon, S.: Architecture for graphical maps of Web contents. In: Proc. WISIS 2004, Warsaw (2004)Google Scholar
  4. 4.
    Ciesielski, K., Draminski, M., Klopotek, M., Kujawiak, M., Wierzchon, S.: Mapping document collections in non-standard geometries. In: De Beats, B., et al. (eds.) Current Issues in Data and Knowledge Engineering, pp. 122–132. Akademicka Oficyna Wydawnicza EXIT Publ., Warszawa (2004)Google Scholar
  5. 5.
    Ciesielski, K., Draminski, M., Klopotek, M., Kujawiak, M., Wierzchon, S.: Clustering medical and biomedical texts - document map based approach. In: Proc. Sztuczna Inteligencja w Inynierii Biomedycznej SIIB 2004, Kraków, Kraków (1905), ISBN-83-919051-5-2Google Scholar
  6. 6.
    Ciesielski, K., Draminski, M., Klopotek, M., Kujawiak, M., Wierzchon, S.T.: On some clustering algorithms for Document Maps Creation. In: Proceedings of the Intelligent Information Processing and Web Mining (IIS:IIPWM 2005), Gdansk (2005)Google Scholar
  7. 7.
    Ciesielski, K., Draminski, M., Klopotek, M., Czerski, D., Wierzchon, S.T.: Adaptive document maps. In: Proc. IIPWM 2006, Ustroń (to appear, 2006)Google Scholar
  8. 8.
    Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 625–632. MIT Press, Cambridge (1995)Google Scholar
  9. 9.
    Hoffmann, T.: Probabilistic Latent Semantic Analysis. In: Proceedings of the 15th Conference on Uncertainty in AI (1999)Google Scholar
  10. 10.
    Hung, C., Wermter, S.: A Constructive and hierarchical self-organising model in a non-stationary environment. In: Int. Joint Conference in Neural Networks (2005)Google Scholar
  11. 11.
    Klopotek, M.: A new Bayesian tree learning method with reduced time and space complexity. Fundamenta Informaticae 49(4), 349–367 (2002)Google Scholar
  12. 12.
    Klopotek, M.: Intelligent information retrieval on the Web. In: Szczepaniak, P.S., Segovia, J., Kacprzyk, J., Zadeh, L.A. (eds.) Intelligent Exploration of the Web, pp. 57–73. Springer, Heidelberg (2003)Google Scholar
  13. 13.
    Klopotek, M., Draminski, M., Ciesielski, K., Kujawiak, M., Wierzchon, S.T.: Mining document maps. In: Gori, M., Celi, M., Nanni, M. (eds.) Proceedings of Statistical Approaches to Web Mining Workshop (SAWM) at PKDD 2004, Pisa, pp. 87–98 (2004)Google Scholar
  14. 14.
    Klopotek, M., Wierzchon, S., Ciesielski, K., Draminski, M., Czerski, D., Kujawiak, M.: Understanding nature of map representation of document collections map quality measurements. In: Proc. Int. Conf. Artificial Intelligence Siedlce (September 2005)Google Scholar
  15. 15.
    Klopotek, M., Wierzchon, S., Ciesielski, K., Draminski, M., Czerski, D.: Conceptual maps and intelligent navigation in document space (in Polish). In: Akademicka Oficyna Wydawnicza EXIT Publishing, Warszawa (to appear, 2006)Google Scholar
  16. 16.
    Kohonen, T.: Self-Organizing Maps. Springer Series in Information Sciences, vol. 30. Springer, Heidelberg (2001)zbMATHGoogle Scholar
  17. 17.
    Koikkalainen, P., Oja, E.: Self-organizing hierarchical feature maps. In: Proc. International Joint Conference on Neural Networks, San Diego, CA, p. 501 (1990), ISBN 3-540-67921-9, ISSN 0720-678X Google Scholar
  18. 18.
    Lagus, K.: Text Mining with WebSOM, PhD Thesis, Helsinki Univ. of Techn. (2000)Google Scholar
  19. 19.
    Rauber, A.: Cluster Visualization in Unsupervised Neural Networks. Diplomarbeit. Technische Universität Wien, Austria (1996)Google Scholar
  20. 20.
    Wise, J.A., Thomas, J., Pennock, J.K., Lantrip, D., Pottier, M., Schur, A., Crow, V.: Visualizing the non-visual: Spatial analysis and interaction with information from text documents. IEEE Information Visualization, 51–58 (1995)Google Scholar
  21. 21.
    Youssefi, A.H., Duke, D.J., Zaki, M.J.: Visual Web Mining. In: WWW 2004, New York, USA, May 17–22 (2004),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mieczysław A. Kłopotek
    • 1
  • Krzysztof Ciesielski
    • 1
  • Dariusz Czerski
    • 1
  • Michał Dramiński
    • 1
  • Sławomir T. Wierzchoń
    • 1
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarszawaPoland

Personalised recommendations