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WWW-Newsgroup-Document Clustering by Means of Dynamic Self-organizing Neural Networks

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Artificial Intelligence and Soft Computing – ICAISC 2008 (ICAISC 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5097))

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Abstract

The paper presents a clustering technique based on dynamic self-organizing neural networks and its application to a large-scale and highly multidimensional WWW-newsgroup-document clustering problem. The collection of 19 997 documents (e-mail messages of different Usenet-News newsgroups) available at WWW server of the School of Computer Science, Carnegie Mellon University (www.cs.cmu.edu/ TextLearning/datasets.html) has been the subject of clustering. A broad comparative analysis with nine alternative clustering techniques has also been carried out demonstrating the superiority of the proposed approach in the considered problem.

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References

  1. Berry, M.W.: Survey of Text Mining. Springer, New York (2004)

    MATH  Google Scholar 

  2. Caillet, M., Pessiot, J., Amini, M., Gallinari, P.: Unsupervised Learning with Term Clustering For Thematic Segmentation of Texts. In: Proc. of RIAO 2004 (Recherche d’Information Assiste par Ordinateur), Toulouse, France (2004)

    Google Scholar 

  3. Chakrabarti, S.: Mining the Web: Analysis of Hypertext and Semi Structured Data. Morgan Kaufmann Publishers, San Francisco (2002)

    Google Scholar 

  4. Franke, J., Nakhaeizadeh, G., Renz, I. (eds.): Text Mining: Theoretical Aspects and Applications. Physica Verlag/Springer, Heidelberg (2003)

    MATH  Google Scholar 

  5. Gorzałczany, M.B., Rudziński, F.: Application of Genetic Algorithms and Kohonen Networks to Cluster Analysis. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 556–561. Springer, Heidelberg (2004)

    Google Scholar 

  6. Gorzałczany, M.B., Rudziński, F.: Modified Kohonen Networks for Complex Cluster-Analysis Problems. In: Rutkowski, L., Siekmann, J., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 562–567. Springer, Heidelberg (2004)

    Google Scholar 

  7. Gorzałczany, M.B., Rudziński, F.: Cluster Analysis Via Dynamic Self-organizing Neural Networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 593–602. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Gorzałczany, M.B., Rudziński, F.: Application of dynamic self-organizing neural networks to WWW-document clustering. International Journal of Information Technology and Intelligent Computing 1(1), 89-101 (2006) (also presented at 8th Int. Conference on Artificial Intelligence and Soft Computing ICAISC 2006, Zakopane)

    Google Scholar 

  9. Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill Book Co., New York (1983)

    MATH  Google Scholar 

  10. Slonim, N., Friedman, N., Tishby, N.: Unsupervised Document Classification using Sequential Informaiton Maximization. In: Proc. of the Twenty-Fifth Annual International ACM SIGIR Conference, Tampere, Finland, pp. 129–136 (2002)

    Google Scholar 

  11. Tang, B., Shepherd, M., Milios, E., Heywood, M.I.: Comparing and combining dimension reduction techniques for efficient text clustering. In: Proc. of Int. Workshop on Feature Selection and Data Mining, Newport Beach (2005)

    Google Scholar 

  12. Weiss, S., Indurkhya, N., Zhang, T., Damerau, F.: Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer, New York (2004)

    Google Scholar 

  13. Zanasi, A. (ed.): Text Mining and its Applications to Intelligence, CRM and Knowledge Management. WIT Press, Southampton (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Kielce University of Technology, Al. 1000-lecia P.P. 7, 25-314, Kielce, Poland

    Marian B. Gorzałczany & Filip Rudziński

Authors
  1. Marian B. Gorzałczany
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  2. Filip Rudziński
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Editor information

Leszek Rutkowski Ryszard Tadeusiewicz Lotfi A. Zadeh Jacek M. Zurada

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Gorzałczany, M.B., Rudziński, F. (2008). WWW-Newsgroup-Document Clustering by Means of Dynamic Self-organizing Neural Networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_5

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  • DOI: https://doi.org/10.1007/978-3-540-69731-2_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69572-1

  • Online ISBN: 978-3-540-69731-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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