  Self-Organizing Maps (SOM) are popular unsupervised artificial neural network
used to reduce dimensions and visualize data. Visual interpretation from
Self-Organizing Maps (SOM) has been limited due to grid approach of data
representation, which makes inter-scenario analysis impossible. The paper
proposes a new way to structure SOM. This model reconstructs SOM to show
strength between variables as the threads of a cobweb and illuminate
inter-scenario analysis. While Radar Graphs are very crude representation of
spider web, this model uses more lively and realistic cobweb representation to
take into account the difference in strength and length of threads. This model
allows for visualization of highly unstructured dataset with large number of
dimensions, common in Bigdata sources.
