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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Baeza-Yates and Ribiero-Neto (1999) Modern Information Retrieval, Addison-Wesley, 1999.
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.
Chakrabarti, S., Dom, B. et al. (1999). Hypersearching the Web. Scientific American, June, 54–60.
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.
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 1999
Kohonen, T. (1997). Self-Organizing Maps. 2nd ed., Springer-Verlag, Berlin.
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.
Salton, G., McGill, M. Introduction to Modern Information Retrieval. McGraw-Hill New-York (1983).
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. 276–282
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.
Zamir, O., and Etzioni O. (1998), Web Document Clustering: A Feasibility Demonstartion, Proceedings of SIGIR 98, Melbourne, Australia.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Goren-Bar, D., Kuflik, T., Lev, D., Shoval, P. (2001). Automating Personal Categorization Using Artificial Neural Networks. In: Bauer, M., Gmytrasiewicz, P.J., Vassileva, J. (eds) User Modeling 2001. UM 2001. Lecture Notes in Computer Science(), vol 2109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44566-8_19
Download citation
DOI: https://doi.org/10.1007/3-540-44566-8_19
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42325-6
Online ISBN: 978-3-540-44566-1
eBook Packages: Springer Book Archive