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

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4093 LNAI, Issue , 2006, Pages 19-30

  Tsoi, A C ^a   Hagenbuchner, M ^b   Sperduti, A ^c  

^a MONASH UNIVERSITY   (Australia)

^b UNIVERSITY OF WOLLONGONG   (Australia)

^c UNIVERSITY OF PADOVA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

DATA PROCESSING; DATA STRUCTURES; DATABASE SYSTEMS; GRAPH THEORY; XML;

GRAPH DATA; GRAPH STRUCTURED INPUTS; KOHONEN; NEURAL NETWORK TECHNIQUES;

SELF ORGANIZING MAPS; DATA MINING;

EID: 33749373226     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11811305_2     Document Type: Conference Paper

References (6)

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2. M. Hagenbuchner, A. Sperduti, and A. Tsoi. Contextual processing of graphs using self-organizing maps. In European symposium on Artificial Neural Networks, Poster track, Bruges, Belgium, 27-29 April 2005.
3. T. Kohonen. Self-Organizing Maps, volume 30 of Springer Series in Information Sciences. Springer, Berlin, Heidelberg, 1995.
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5. D. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 1st edition, 2003.
6. S. A. Schlkopf, B. Learning with Kernels. MIT Press, Cambridge, MA, 1st edition, 2002.