Verifying the Proximity and Size Hypothesis for Self-Organizing Maps

Chienting Lin, Hsinchun Chen, Jay F. Nunamaker

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


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 one another. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection and to present major topics within the collection with larger regions. This article presents research in which we sought to validate these properties of SOM, called the Proximity and Size Hypotheses, through a user evaluation study. Building upon our previous research in automatic concept generation and classification, we demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall7 scores as judged by human experts. We also demonstrated a positive relationship between the size of an SOM region and the number of documents contained in the region. We believe this research has established the Kohonen SOM algorithm as an intuitively appealing and promising neural-network-based textual classification technique for addressing part of the longstanding "information overload" problem.

Original languageEnglish (US)
Pages (from-to)57-70
Number of pages14
JournalJournal of Management Information Systems
Issue number3
StatePublished - 1999


  • Document clustering techniques
  • Experimental research
  • Group support systems
  • Self-organizing maps
  • Unsupervised learning algorithms

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management


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