Self-organizing maps
The distributed object computing paradigm: concepts and applications
Journal of Systems and Software - Special issue on invited articles on top systems and software engineering scholars
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
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The self-organization map (SOM) neural network has been recognized as a successful paradigm for clustering and visualization in a large variety of real-world applications. There exist a number of useful stand-alone SOM tools, however, they cannot be adapted to the new-generation web environment. In addition, different user interfaces required for operation and the heterogeneity of platforms where the tools run on prevent them from appeal. In this paper, we propose a web-aware SOM tool which integrates the computationally powerful SOM_PAK and the vivid Nenet tools to augment the advantages of each. The proposed SOM tool is capable of delimiting the desired clusters by adopting two-level network topology and silhouette coefficients.