Techniques and Technologies Behind Maps of Internet and Intranet Document Collections

  • Mieczyslaw A. Kłopotek
  • Sławomir T. Wierzchon
  • Krzysztof Ciesielski
  • Michal Dramiński
  • Dariusz Czerski
Part of the Studies in Computational Intelligence book series (SCI, volume 37)


Bayesian Network Document Collection Contextual Model Normalize Mutual Information Average Path Length 
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.


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  1. 1.
    Aggarwal C, Al-Garawi F, Yu P (2001) Intelligent crawling on the World Wide Web with arbitrary predicates. In: Proc. 10th International World Wide Web Conference, 96-105.Google Scholar
  2. 2.
    Becks A (2001) Visual Knowledge Management with Adaptable Document Maps. GMD Research Series, Sank AugustinGoogle Scholar
  3. 3.
    Berry M, Drmac Z, IJessup E (1999) Matrices, vector spaces and information retrieval. SIAM Review 41: 2: 335-362zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Bezdek J, Pal S (1992) Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data. IEEE, New YorkGoogle Scholar
  5. 5.
    Boulis C, Ostendorf M (2004) Combining multiple clustering systems. In: Pro-ceedings of 8th European conference on Principles and Practice of Knowledge Discovery in Databases (PKDD-2004), LNAI 3202, Springer-VerlagGoogle Scholar
  6. 6.
    Chen J, Sun L, Zaiane O, Goebel R (2004) Visualizing and Discovering Web Navigational Patterns. Scholar
  7. 7.
    Chow C, Liu C (1968) Approximating discrete probability distributions with dependence trees. IEEE Transactions on IT, IT-14: 3: 462-467CrossRefGoogle Scholar
  8. 8.
    Ciesielski K, Draminski M,Klopotek M, Kujawiak M, Wierzchon S (2004) Ar-chitecture for graphical maps of Web contents. In: Proc. WISIS’2004, Warsaw.Google Scholar
  9. 9.
    Ciesielski K, Draminski M, Klopotek M, Kujawiak M, Wierzchon S (2004) Map-ping document collections in non-standard geometries. In: De Beats B, De Caluwe R, de Tre G, Fodor J, Kacprzyk J, Zadrony S (eds): Current Issues in Data and Knowledge Engineering. Akademicka Oficyna Wydawnicza EXIT Publ., Warszawa, 122-132Google Scholar
  10. 10.
    Ciesielski K, Draminski M,Klopotek M, Kujawiak M, Wierzchon S (2004) Clus- tering medical and biomedical texts - document map based approach. In: Proc. Sztuczna Inteligencja w Inynierii Biomedycznej SIIB’04, Kraków. ISBN-83- 919051-5-2Google Scholar
  11. 11.
    Ciesielski K, Draminski M,Klopotek M, Kujawiak M, Wierzchon S (2005) On some clustering algorithms for Document Maps Creation. In: Proceedings of the Intelligent Information Processing and Web Mining (IIS:IIPWM-2005), Gdansk, 2005Google Scholar
  12. 12.
    Ciesielski K, Draminski M,Klopotek M, Kujawiak M, Wierzchon S (2005) Co-existence of crisp and fuzzy concepts in document maps. in: Duch w, Kacprzyk J, eds, Proc. ICAINN, LNCS vol. 3697/2005, Springer Verlag, part II pp. 859.Google Scholar
  13. 13.
    Ciesielski K, Draminski M, Klopotek M, Czerski D, Wierzchon S (2006) Adap-tive document maps. To appear in: Proceedings of the Intelligent Information Processing and Web Mining (IIS:IIPWM-2006), UstronGoogle Scholar
  14. 14.
    Cohn D, Hofmann T (2001) The missing link - a probabilistic model of document content and hypertext connectivity. In: Leen T, Dietterich T, Tresp V (eds): Advances in Neural Information Processing Systems, Vol. 10,
  15. 15.
    Deerwester S, Dumais S, Landauer T, Furnas G, Harshman R (1990) Indexing by Latent Semantic Analysis. Journal of the American Society of Information Sci-ence, 41 (1990) 6: 391-407 Scholar
  16. 16.
    Dittenbach M, Rauber A, Merkl D (2002) Uncovering hierarchical structure in data using the Growing Hierarchical Self-Organizing Map. Neurocomputing 48 (1-4): 199-216.zbMATHCrossRefGoogle Scholar
  17. 17.
    Dubois D, Prade H (1980) Fuzzy Sets and Systems. Theory and Applications, Academic PressGoogle Scholar
  18. 18.
    Fritzke B (1995) A growing neural gas network learns topologies. In: Tesauro G, Touretzky D, Leen T (Eds.): Advances in Neural Information Processing Systems 7, MIT Press Cambridge, MA, 625-632.Google Scholar
  19. 19.
    . Fritzke B (1996) Some competitive learning methods, draft available from
  20. 20.
    Fritzke B (1997) A self-organizing network that can follow non-stationary dis- tributions. In: Proceeding of the International Conference on Artificial Neural Networks ’97, Springer, 613-618Google Scholar
  21. 21.
    Halkidi M, Batistakis Y, Vazirgiannis M (2001) On clustering validation tech- niques. Journal of Intelligent Information Systems, 17 (2-3): 107-145zbMATHCrossRefGoogle Scholar
  22. 22.
    Hoffmann T (1999) Probabilistic Latent Semantic Analysis. In: Proceedings of the 15th Conference on Uncertainty in AI.Google Scholar
  23. 23.
    Hung C, Wermter S (2005) A constructive and hierarchical self-organising model in a non-stationary environment. In: International Joint Conference in Neural NetworksGoogle Scholar
  24. 24.
    Klopotek M (2002) A new Bayesian tree learning method with reduced time and space complexity. Fundamenta Informaticae, 49 (4): 349-367zbMATHMathSciNetGoogle Scholar
  25. 25.
    Klopotek M, Draminski M, Ciesielski K, Kujawiak M, Wierzchon S (2004) Min-ing document maps. In: Proceedings of Statistical Approaches to Web Mining Workshop (SAWM) at PKDD’04, M. Gori, M. Celi, M. Nanni (eds.), Pisa, 87-98Google Scholar
  26. 26.
    Klopotek M, Wierzchon S, Ciesielski K, Draminski M, Czerski D, Kujawiak M (2005) Understanding nature of map representation of document collections map quality measurements. In: Proc. Int.Conf. Artificial Intelligence SiedlceGoogle Scholar
  27. 27.
    Klopotek M, Wierzchon S, Ciesielski K, Draminski M, Czerski D (2006) Concep-tual maps and intelligent navigation in document space (in Polish). To appear in: Akademicka Oficyna Wydawnicza EXIT Publishing, Warszawa.Google Scholar
  28. 28.
    Kohonen T (2001) Self-Organizing Maps. Springer Series in Information Sci-ences, vol. 30, Springer, Berlin, Heidelberg, New York.Google Scholar
  29. 29.
    Kohonen T, Kaski S, Somervuo P, Lagus K, Oja M, Paatero V (2003) Self-organization of very large document collections. Helsinki University of Technology technical report.
  30. 30.
    Rauber A (1996) Cluster Visualization in Unsupervised Neural Networks. Diplo-marbeit, Technische Universitt Wien, AustriaGoogle Scholar
  31. 31.
    Timmis J ( 2001) aiVIS: Artificial Immune Network Visualization. In: Proceed-ings of EuroGraphics UK 2001 Conference, Univeristy College London, 61-69Google Scholar
  32. 32.
    Wierzchon S (2001) Artificial immune systems. Theory and applications (in Polish), ICS PAS Publishing House.Google Scholar
  33. 33.
    Wise J, Thomas J, Pennock K, Lantrip D, Pottier M, Schur A, Crow V (1995) Visualizing the non-visual: Spatial analysis and interaction with information from text documents. In: IEEE Information Visualization. 51-58.Google Scholar
  34. 34.
    Youssefi A, Duke D, Zaki M (2004) Visual Web Mining., WWW2004, May 1722, 2004, New York, NY USA.
  35. 35.
    Zhang T, Ramakrishan R, Livny M (1997) BIRCH: Efficient data clustering method for large databases. In: Proceedings of ACM SIGMOD International Conference on Data Management.Google Scholar
  36. 36.
    Zhao Y, Karypis G (2005) Criterion functions for document clustering: Ex-periments and analysis, available at
  37. 37.
  38. 38.
  39. 39.
  40. 40.

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Mieczyslaw A. Kłopotek
    • 1
  • Sławomir T. Wierzchon
    • 2
  • Krzysztof Ciesielski
    • 3
  • Michal Dramiński
    • 4
  • Dariusz Czerski
    • 5
  1. 1.Polish Academy of SciencesInstitute of Computer ScienceWarszawaPoland
  2. 2.Polish Academy of SciencesInstitute of Computer ScienceWarszawaPoland
  3. 3.Polish Academy of SciencesInstitute of Computer ScienceWarszawaPoland
  4. 4.Polish Academy of SciencesInstitute of Computer ScienceWarszawaPoland
  5. 5.Polish Academy of SciencesInstitute of Computer ScienceWarszawaPoland

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