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Automating Personal Categorization Using Artificial Neural Networks

  • Dina Goren-Bar
  • Tsvi Kuflik
  • Dror Lev
  • Peretz Shoval
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2109)

Abstract

Organizations as well as personal users invest a great deal of time in assigning documents they read or write to categories. Automatic document classification that matches user subjective classification is widely used, but much challenging research still remain to be done. The self-organizing map (SOM) is an artificial neural network (ANN) that is mathematically characterized by transforming high-dimensional data into two-dimensional representation. This enables automatic clustering of the input, while preserving higher order topology. A closely related method is the Learning Vector Quantization (LVQ) algorithm, which uses supervised learning to maximize correct data classification. This study evaluates and compares the application of SOM and LVQ to automatic document classification, based on a subjectively predefined set of clusters in a specific domain. A set of documents from an organization, manually clustered by a domain expert, was used in the experiment. Results show that in spite of the subjective nature of human categorization, automatic document clustering methods match with considerable success subjective, personal clustering, the LVQ method being more advantageous.

Keywords

Artificial Neural Network Data Item Output Unit Learn Vector Quantization Cluster Label 
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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References

  1. 1.
    Baeza-Yates and Ribiero-Neto (1999) Modern Information Retrieval, Addison-Wesley, 1999.Google Scholar
  2. 2.
    Boger, Z. Kuflik, T., Shapira, B. and Shoval, P. (2000) Information Filtering and Automatic Keywords Identification by Artificial Neural Networks Proccedings of the 8th Europian Conference on Information Systems. pp. 46–52, Vienna, July 2000.Google Scholar
  3. 3.
    Chakrabarti, S., Dom, B. et al. (1999). Hypersearching the Web. Scientific American, June, 54–60.Google Scholar
  4. 4.
    Honkela, T., Kaski, S., Lagus, K., and Kohonen, T. (1997). WEBSOM-Self-Organizing Maps of Document Collections. In Proceedings of WSOM’97, Workshop on Self-Organizing Maps, Espoo, Finland, June 4-6, pages 310–315. Helsinki University of Technology, Neural Networks Research Centre, Espoo, Finland.Google Scholar
  5. 5.
    Jain, A.K., Murty, M.N., Flynn, P.J. (1999) Data Clustering: A Review, ACM Computing Surveys, Vol 31,No. 3 pp. 264–323, September 1999CrossRefGoogle Scholar
  6. 6.
    Kohonen, T. (1997). Self-Organizing Maps. 2nd ed., Springer-Verlag, Berlin.zbMATHGoogle Scholar
  7. 7.
    Rauber A. and Merkl. D. (1999). Using self-organizing maps to organize document archives and to characterize subject matters: How to make a map tell the news of the world Proceedings of the 10th Intl. Conf. on Database and Expert Systems Applications (DEXA’99), Florence, Italy.Google Scholar
  8. 8.
    Salton, G., McGill, M. Introduction to Modern Information Retrieval. McGraw-Hill New-York (1983).zbMATHGoogle Scholar
  9. 9.
    Segal, B.R., and Kephart, J.O., (1999) MailCat: An Intelligent Assistant for Organizing Email, Proceedings of the Third International Conference on Autonomous Agents. Pp. 276–282Google Scholar
  10. 10.
    Vesanto, J., Alhoniemi, E., Himberg, J., Kiviluoto, K., & Parviainen, J. (1999). Self-Organizing Map for Data Mining in Matlab: The SOM Toolbox. Simulation News Europe, (25):54.Google Scholar
  11. 11.
    Zamir, O., and Etzioni O. (1998), Web Document Clustering: A Feasibility Demonstartion, Proceedings of SIGIR 98, Melbourne, Australia.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Dina Goren-Bar
    • 1
  • Tsvi Kuflik
    • 1
  • Dror Lev
    • 1
  • Peretz Shoval
    • 1
  1. 1.Department of Information Systems EngineeringBen Gurion University of the NegevBeer-ShevaIsrael

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