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Proceedings Paper

Using Kohonen networks for WWW document classification
Author(s): Filip Rudzinski; Adam Gluszek; Michal Kekez
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Paper Abstract

This paper presents flexible solutions to the clustering and classification of World Wide Web documents. The method proposed in this paper applies the self-organizing Kohonen network known also a self-organizing map (SOM) with two-layer architecture. In this architecture documents become mapped as points on the SOM, in a geometric order that describes the similarity of their contents. This network has been learned by means of unsupervised training technique. After learning process has been completed, the network visualizes semantic relationship between input documents as two-dimensional semantic map. This map is a retrieval interface for an online WWW documents classification system. In this paper, first, the main idea of solution based on SOM has been presented. Next, the operation of this method has been illustrated with the us of synthetic data set. Finally, this technique has been tested by means of real-life WWW documents set.

Paper Details

Date Published: 23 February 2005
PDF: 5 pages
Proc. SPIE 5775, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments III, (23 February 2005); doi: 10.1117/12.610756
Show Author Affiliations
Filip Rudzinski, Kielce Univ. of Technology (Poland)
Adam Gluszek, Kielce Univ. of Technology (Poland)
Michal Kekez, Kielce Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 5775:
Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments III
Ryszard S. Romaniuk, Editor(s)

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