Document clustering for electronic meetings: an experimental comparison of two techniques
| dc.contributor.author | Roussinov, Dmitri G. | |
| dc.contributor.author | Chen, Hsinchun | |
| dc.date.accessioned | 2004-09-04T00:00:01Z | |
| dc.date.available | 2010-06-18T23:19:15Z | |
| dc.date.issued | 1999-11 | en_US |
| dc.date.submitted | 2004-09-04 | en_US |
| dc.identifier.citation | Document clustering for electronic meetings: an experimental comparison of two techniques 1999-11, 27(1-2):67-80 Decision Support Systems | en_US |
| dc.identifier.uri | http://hdl.handle.net/10150/105091 | |
| dc.description | Artificial Intelligence Lab, Department of MIS, University of Arizona | en_US |
| dc.description.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. | |
| dc.format.mimetype | application/pdf | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Elsevier | en_US |
| dc.subject | World Wide Web | en_US |
| dc.subject | Classification | en_US |
| dc.subject.other | National Science Digital Library | en_US |
| dc.subject.other | NSDL | en_US |
| dc.subject.other | Artificial intelligence lab | en_US |
| dc.subject.other | AI lab | en_US |
| dc.subject.other | Group decision support systems | en_US |
| dc.subject.other | Text document clustering | en_US |
| dc.subject.other | Empirical study | en_US |
| dc.subject.other | Self-organizing maps | en_US |
| dc.subject.other | Neural networks | en_US |
| dc.subject.other | Cluster analysis | en_US |
| dc.title | Document clustering for electronic meetings: an experimental comparison of two techniques | en_US |
| dc.type | Journal Article (Paginated) | en_US |
| dc.identifier.journal | Decision Support Systems | en_US |
| refterms.dateFOA | 2018-06-12T00:39:06Z | |
| html.description.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. |
