Abstract
Methods for the automatic categorization of documents are usually based on a simple analysis of the considered document collection. User specific criteria, e.g. interests in specific topics or keywords, are usually neglected. Therefore, the resulting categorization frequently does not fulfil the user expectancies. In prior work we had developed an approach to cluster document collections by growing self-organizing maps that adapt their structure automatically to the structure and size of the underlying document collection. In this paper, we present an approach to improve the obtained clustering by considering user feedback (in the form of drag-and-drop) to adapt the underlying topology and thus the categorization of documents by the self-organizing map. Furthermore, we briefly present applications for image and text document collections.
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Nürnberger, A. (2004). User Adaptive Categorization of Document Collections. In: Nürnberger, A., Detyniecki, M. (eds) Adaptive Multimedia Retrieval. AMR 2003. Lecture Notes in Computer Science, vol 3094. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25981-7_6
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DOI: https://doi.org/10.1007/978-3-540-25981-7_6
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