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Self-organising Map Techniques for Graph Data Applications to Clustering of XML Documents

  • A. C. Tsoi
  • M. Hagenbuchner
  • A. Sperduti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

In this paper, neural network techniques based on Kohonen’s self-organising map method which can be trained in an unsupervised fashion and applicable to the processing of graph structured inputs are described. Then it is shown how such techniques can be applied to the problems of clustering of XML documents.

Keywords

Root Node Input Vector Compression Ratio Neural Network Technique String Kernel 
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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References

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    Hagenbuchner, M., Sperduti, A., Tsoi, A.: A self-organizing map for adaptive processing of structured data. IEEE Transactions on Neural Networks 14(3), 491–505 (2003)CrossRefGoogle Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. C. Tsoi
    • 1
  • M. Hagenbuchner
    • 2
  • A. Sperduti
    • 3
  1. 1.Monash e-Research CentreMonash UniversityVictoriaAustralia
  2. 2.Faculty of InformaticsUniversity of WollongongWollongong NSW 2522Australia
  3. 3.Dipartimento di Matematica Pura ed ApplicataUniversity of PadovaPadovaItaly

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