TY - JOUR
T1 - Document clustering for electronic meetings
T2 - An experimental comparison of two techniques
AU - Roussinov, Dmitri G.
AU - Chen, Hsinchun
N1 - Funding Information:
Our research was supported by: Digital Library Initiative grant awarded by NSF/ARPA/NASA (“Building the Interspace: Digital Library Infrastructure for a University Engineering Community,” PIs: B. Schatz, H. Chen et al., 1994–1998, IRI9411318); NSF/CISE grant (“Concept-based Categorization and Search on Internet: A Machine Learning, Parallel Computing Approach,” PI: H. Chen, 1995–1998, IRI9525790.
Funding Information:
Dr. Hsinchun Chen is McClelland Professor of MIS and Andersen Professor of MIS at the University of Arizona, where he directs the UA/MIS Artificial Intelligence Group. Professor Chen is a Visiting Senior Research Scientist at NCSA. His articles have appeared in Communications of the ACM, IEEE Computer, Journal of the American Society for Information Science, IEEE Expert and many other publications. He has won numerous awards including Research Initiation (NSF), Best Paper (HICSS 1992), and an AT&T Foundation Award in Science and Engineering in 1994 and 1995. Professor Chen is a PI of the Illinois Digital Library Initiative project, and has received grant awards from the NSF, DARPA, NASA, NIH, NCSA, and NIJ. He is guest editor for special issues of IEEE Computer and the Journal of the American Society for Information Science. Dmitri Roussinov is an Assistant Professor at the School of Information Studies, Syracuse University. His research interests, include human computer interaction, neural networks, and information retrieval. He has received an M.S. and B.S. in Computer Science from Moscow Institute of Physics and Technology, M.A. in Economics from Indiana University, and Ph.D. in Information Systems from the University of Arizona.
PY - 1999/11
Y1 - 1999/11
N2 - 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.
AB - 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.
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U2 - 10.1016/S0167-9236(99)00037-8
DO - 10.1016/S0167-9236(99)00037-8
M3 - Article
AN - SCOPUS:0033336516
SN - 0167-9236
VL - 27
SP - 67
EP - 79
JO - Decision Support Systems
JF - Decision Support Systems
IS - 1
ER -