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23 February 2005 Using Kohonen networks for WWW document classification
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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.
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Filip Rudzinski, Adam Gluszek, and Michal Kekez "Using Kohonen networks for WWW document classification", Proc. SPIE 5775, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments III, (23 February 2005); https://doi.org/10.1117/12.610756
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