Leveraging a web-aware self-organization map tool for clustering and visualization

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.