Verifying the proximity hypothesis for self-organizing maps
Chienting Lin, Hsinchun Chen, Jay F. Nunamaker · 2003
The Kohonen self-organizing map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to each other. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection. This article presents research in which we sought to validate this property of SOM, called the proximity hypothesis, through a user evaluation study. Built upon our previous research in automatic concept generation and classification, we demonstrated that the Kohenen SOM was able to perform concept clustering effectively, based on its concept precision and recall scores judged by human experts. We believe this research has established Kohonen SOM algorithm as an intuitively appealing and promising neural network based textual classification technique for addressing part of the long-standing "information overload" problem.