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

Artificial Intelligence Lab, Department of MIS, University of Arizona / In this article, we report our implementation and comparison of two text clustering techniques. One is based on Wardâ s clustering and the other on Kohonenâ 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 â â clean upâ â the automatically produced clusters. The technique based on Wardâ 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.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/105091
Date11 1900
CreatorsRoussinov, Dmitri G., Chen, Hsinchun
PublisherElsevier
Source SetsUniversity of Arizona
LanguageEnglish
Detected LanguageEnglish
TypeJournal Article (Paginated)

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