Verifying the proximity hypothesis for self-organizing maps

Chienting Lin, Hsinchun Chen, Jay Nunamaker

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


The Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data. 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. We demonstrated that the Kohonen 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 the Kohonen SOM algorithm a promising textual classification technique for addressing the long-standing `information overload' problem.

Original languageEnglish (US)
Pages (from-to)33
Number of pages1
JournalProceedings of the Hawaii International Conference on System Sciences
StatePublished - 1999
EventProceedings of the 1999 32nd Annual Hawaii International Conference on System Sciences, HICSS-32 - Maui, HI, USA
Duration: Jan 5 1999Jan 8 1999

ASJC Scopus subject areas

  • General Computer Science


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