Self-Organising Maps for Hierarchical Tree View Document Clustering Using Contextual Information

  • Richard Freeman
  • Hujun Yin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)


In this paper we propose an effective method to cluster documents into a dynamically built taxonomy of topics, directly extracted from the documents. We take into account short contextual information within the text corpus, which is weighted by importance and used as input to a set of independently spun growing Self-Organising Maps (SOM). This work shows an increase in precision and labelling quality in the hierarchy of topics, using these indexing units. The use of the tree structure over sets of conventional two-dimensional maps creates topic hierarchies that are easy to browse and understand, in which the documents are stored based on their content similarity.


Feature Selection Random Projection Document Cluster Text Corpus Weighting Bias 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Richard Freeman
    • 1
  • Hujun Yin
    • 1
  1. 1.Department of Electrical Engineering and ElectronicsUniversity of Manchester Institute of Science and Technology (UMIST)ManchesterUK

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