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Self organizing maps for the clustering of large sets of labeled graphs

Journal Article


Abstract


  • Graph Self-Organizing Maps (GraphSOMs) are a new concept in the processing of structured objects using machine learning methods. The GraphSOM is a generalization of the Self-Organizing Maps for Structured Domain (SOM-SD) which had been shown to be a capable unsupervised machine learning method for some types of graph

    structured information. An application of the SOM-SD to document mining tasks as part of an international competition: Initiative for the Evaluation of XML Retrieval (INEX), on the clustering of XML formatted documents was conducted, and the method subsequently won the competition in 2005 and 2006 respectively. This paper applies the GraphSOM to the

    clustering of a larger dataset in the INEX competition 2007. The results are compared with those obtained when utilizing the more traditional SOM-SD approach. Experimental results show that (1) the GraphSOM is computationally more efficient than the SOM-SD, (2) the performances of both approaches on the larger dataset in INEX 2007 are not competitive when compared with those obtained by other participants of the competition using other approaches, and, (3)

    different structural representation of the same dataset can influence the performance of the proposed GraphSOM technique.

Publication Date


  • 2009

Citation


  • Zhang, S., Hagenbuchner, M., Tsoi, A. & Kc, M. (2009). Self organizing maps for the clustering of large sets of labeled graphs. In Advances in Focused Retrieval, Proceedings of the 7th International Workshop of the Initiative for the Evaluation of XML Retrieval (INEX 2008), 15-18 Dec 2008, Dagstuhl Castle, Germany. Lecture Notes in Computer Science, 5631 469-481.

Scopus Eid


  • 2-s2.0-70350480761

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1714

Has Global Citation Frequency


Number Of Pages


  • 12

Start Page


  • 469

End Page


  • 481

Volume


  • 5631

Place Of Publication


  • Germany

Abstract


  • Graph Self-Organizing Maps (GraphSOMs) are a new concept in the processing of structured objects using machine learning methods. The GraphSOM is a generalization of the Self-Organizing Maps for Structured Domain (SOM-SD) which had been shown to be a capable unsupervised machine learning method for some types of graph

    structured information. An application of the SOM-SD to document mining tasks as part of an international competition: Initiative for the Evaluation of XML Retrieval (INEX), on the clustering of XML formatted documents was conducted, and the method subsequently won the competition in 2005 and 2006 respectively. This paper applies the GraphSOM to the

    clustering of a larger dataset in the INEX competition 2007. The results are compared with those obtained when utilizing the more traditional SOM-SD approach. Experimental results show that (1) the GraphSOM is computationally more efficient than the SOM-SD, (2) the performances of both approaches on the larger dataset in INEX 2007 are not competitive when compared with those obtained by other participants of the competition using other approaches, and, (3)

    different structural representation of the same dataset can influence the performance of the proposed GraphSOM technique.

Publication Date


  • 2009

Citation


  • Zhang, S., Hagenbuchner, M., Tsoi, A. & Kc, M. (2009). Self organizing maps for the clustering of large sets of labeled graphs. In Advances in Focused Retrieval, Proceedings of the 7th International Workshop of the Initiative for the Evaluation of XML Retrieval (INEX 2008), 15-18 Dec 2008, Dagstuhl Castle, Germany. Lecture Notes in Computer Science, 5631 469-481.

Scopus Eid


  • 2-s2.0-70350480761

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/1714

Has Global Citation Frequency


Number Of Pages


  • 12

Start Page


  • 469

End Page


  • 481

Volume


  • 5631

Place Of Publication


  • Germany