Document clustering for electronic meetings: An experimental comparison of two techniques

Dmitri G. Roussinov, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

87 Scopus citations


In this article, we report our implementation and comparison of two text clustering techniques. One is based on Ward's clustering and the other on Kohonen's Self-organizing Maps. We have evaluated how closely clusters produced by a computer resemble those created by human experts. We have also measured the time that it takes for an expert to `clean up' the automatically produced clusters. The technique based on Ward's clustering was found to be more precise. Both techniques have worked equally well in detecting associations between text documents. We used text messages obtained from group brainstorming meetings.

Original languageEnglish (US)
Pages (from-to)67-79
Number of pages13
JournalDecision Support Systems
Issue number1
StatePublished - Nov 1999

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


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