Self Organizing Maps for the Clustering of Large Sets of Labeled Graphs

  • ShuJia Zhang
  • Markus Hagenbuchner
  • Ah Chung Tsoi
  • Alessandro Sperduti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5631)


Data mining on Web documents is one of the most challenging tasks in machine learning due to the large number of documents on the Web, the underlying structures (as one document may refer to another document), and the data is commonly not labeled (the class in which the document belongs is not known a-priori). This paper considers latest developments in Self-Organizing Maps (SOM), a machine learning approach, as one way to classifying documents on the Web. The most recent development is called a Probability Mapping Graph Self-Organizing Map (PMGraphSOM), and is an extension of an earlier Graph-SOM approach; this encodes undirected and cyclic graphs in a scalable fashion. This paper illustrates empirically the advantages of the PMGraphSOM versus the original GraphSOM model in a data mining application involving graph structured information. It will be shown that the performances achieved can exceed the current state-of-the art techniques on a given benchmark problem.


Cluster Performance Data Label Label Graph Neighborhood Function 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 2009

Authors and Affiliations

  • ShuJia Zhang
    • 1
  • Markus Hagenbuchner
    • 1
  • Ah Chung Tsoi
    • 2
  • Alessandro Sperduti
    • 3
  1. 1.University of WollongongWollongongAustralia
  2. 2.Hong Kong Baptist UniversityHong Kong
  3. 3.University of PadovaPadovaItaly

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