Reconstructing Self Organizing Maps as Spider Graphs for better visual interpretation of large unstructured datasets

TL;DR

This paper introduces a novel cobweb-like visualization method for Self-Organizing Maps, enhancing the interpretation of large, unstructured datasets by illustrating variable relationships more vividly than traditional grid approaches.

Contribution

It reconstructs SOMs into spider graph structures, enabling better inter-scenario analysis and visualization of high-dimensional data.

Findings

Enhanced visualization of variable relationships

Improved interpretability of large datasets

More realistic and informative cobweb representations

Abstract

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.