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Document Clustering Using the 1 + 1 Dimensional Self-Organising Map

  • Ben Russell
  • Hujun Yin
  • Nigel M. Allinson
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)

Abstract

Automatic clustering of documents is a task that has become increasingly important with the explosion of online information. The Self Organising Map (SOM) has been used to cluster documents effectively, but efforts to date have used a single or a series of 2-dimensional maps. Ideally, the output of a document-clustering algorithm should be easy for a user to interpret. This paper describes a method of clustering documents using a series of 1-dimensional SOM arranged hierarchically to provide an intuitive tree structure representing document clusters. Wordnet is used to find the base forms of words and only cluster on words that can be nouns.

Keywords

Quantisation Error Vector Space Model Document Cluster Document Vector Winning Neuron 
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

  • Ben Russell
    • 1
  • Hujun Yin
    • 1
  • Nigel M. Allinson
    • 1
  1. 1.Department of Electrical Engineering and ElectronicsUniversity of Manchester Institute of Science and Technology (UMIST)ManchesterUK

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