English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Self-Organizing Maps (SOM) are popular unsupervised artificial neural networkused to reduce dimensions and visualize data. Visual interpretation fromSelf-Organizing Maps (SOM) has been limited due to grid approach of datarepresentation, which makes inter-scenario analysis impossible. The paperproposes a new way to structure SOM. This model reconstructs SOM to showstrength between variables as the threads of a cobweb and illuminateinter-scenario analysis. While Radar Graphs are very crude representation ofspider web, this model uses more lively and realistic cobweb representation totake into account the difference in strength and length of threads. This modelallows for visualization of highly unstructured dataset with large number ofdimensions, common in Bigdata sources.

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