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

Persistent Link:
http://hdl.handle.net/10150/105091
Title:
Document clustering for electronic meetings: an experimental comparison of two techniques
Author:
Roussinov, Dmitri G.; Chen, Hsinchun
Citation:
Document clustering for electronic meetings: an experimental comparison of two techniques 1999-11, 27(1-2):67-80 Decision Support Systems
Publisher:
Elsevier
Journal:
Decision Support Systems
Issue Date:
Nov-1999
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/105091
Submitted date:
2004-09-04
Abstract:
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.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
World Wide Web; Classification
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab; Group decision support systems; Text document clustering; Empirical study; Self-organizing maps; Neural networks; Cluster analysis

Full metadata record

DC FieldValue Language
dc.contributor.authorRoussinov, Dmitri G.en_US
dc.contributor.authorChen, Hsinchunen_US
dc.date.accessioned2004-09-04T00:00:01Z-
dc.date.available2010-06-18T23:19:15Z-
dc.date.issued1999-11en_US
dc.date.submitted2004-09-04en_US
dc.identifier.citationDocument clustering for electronic meetings: an experimental comparison of two techniques 1999-11, 27(1-2):67-80 Decision Support Systemsen_US
dc.identifier.urihttp://hdl.handle.net/10150/105091-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractIn 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.en_US
dc.format.mimetypeapplication/pdfen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectWorld Wide Weben_US
dc.subjectClassificationen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherGroup decision support systemsen_US
dc.subject.otherText document clusteringen_US
dc.subject.otherEmpirical studyen_US
dc.subject.otherSelf-organizing mapsen_US
dc.subject.otherNeural networksen_US
dc.subject.otherCluster analysisen_US
dc.titleDocument clustering for electronic meetings: an experimental comparison of two techniquesen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalDecision Support Systemsen_US
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