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Self organizing maps for the clustering of large sets of labaeled groups

Conference Paper


Abstract


  • 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 GraphSOM

    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.

Publication Date


  • 2009

Citation


  • Zhang, S., Hagenbuchner, M., Tsoi, A. Chung. & Sperduti, A. (2009). Self organizing maps for the clustering of large sets of labaeled groups. In S. Geva, J. Kamps & A. Trotman (Eds.), 7th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2008 (pp. 469-481). Berlin, Heidelberg: Springer-Verlag.

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3586

Start Page


  • 469

End Page


  • 481

Abstract


  • 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 GraphSOM

    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.

Publication Date


  • 2009

Citation


  • Zhang, S., Hagenbuchner, M., Tsoi, A. Chung. & Sperduti, A. (2009). Self organizing maps for the clustering of large sets of labaeled groups. In S. Geva, J. Kamps & A. Trotman (Eds.), 7th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2008 (pp. 469-481). Berlin, Heidelberg: Springer-Verlag.

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/3586

Start Page


  • 469

End Page


  • 481