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Self-organising map techniques for graph data applications to clustering of XML documents

Conference Paper


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. © Springer-Verlag Berlin Heidelberg 2006.

Publication Date


  • 2006

Citation


  • Tsoi, A. C., Hagenbuchner, M., & Sperduti, A. (2006). Self-organising map techniques for graph data applications to clustering of XML documents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 4093 LNAI (pp. 19-30). doi:10.1007/11811305_2

Scopus Eid


  • 2-s2.0-33749373226

Start Page


  • 19

End Page


  • 30

Volume


  • 4093 LNAI

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. © Springer-Verlag Berlin Heidelberg 2006.

Publication Date


  • 2006

Citation


  • Tsoi, A. C., Hagenbuchner, M., & Sperduti, A. (2006). Self-organising map techniques for graph data applications to clustering of XML documents. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 4093 LNAI (pp. 19-30). doi:10.1007/11811305_2

Scopus Eid


  • 2-s2.0-33749373226

Start Page


  • 19

End Page


  • 30

Volume


  • 4093 LNAI