Self-Organizing Maps
Graph Neural Networks for Ranking Web Pages
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
Clustering XML documents using self-organizing maps for structures
INEX'05 Proceedings of the 4th international conference on Initiative for the Evaluation of XML Retrieval
Self-organizing maps for learning the edit costs in graph matching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A self-organizing map for adaptive processing of structured data
IEEE Transactions on Neural Networks
Document Clustering Using Incremental and Pairwise Approaches
Focused Access to XML Documents
A machine learning approach to link prediction for interlinked documents
INEX'09 Proceedings of the Focused retrieval and evaluation, and 8th international conference on Initiative for the evaluation of XML retrieval
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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.