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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2000 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 2000 Working Papers
      • View Item
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      Hierarchical document clustering using automatically extracted keyphrases

      Jones, Steve; Mahoui, Malika
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      Jones, S. & Mahoui, M. (2000). Hierarchical document clustering using automatically extracted keyphrases. (Working paper 00/13). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1029
      Abstract
      In this paper we present a technique for automatically generating hierarchical clusters of documents. Our technique exploits document keyphrases as features of the document space to support clustering. In fact, we cluster keyphrases rather than documents themselves and then associate documents with keyphrase clusters. We discuss alternative measures of similarity between ‘soft-clusters’ which seed Ward’s hierarchical clustering algorithm, and present the resulting cluster hierarchies that we have produced for a large collection of scientific technical reports. We analyse the effect of the alternative similarity measures and suggest improvement to our technique.
      Date
      2000-10
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      00/13
      Publisher
      University of Waikato, Department of Computer Science
      Collections
      • 2000 Working Papers [12]
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