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On detecting differences between groups

Webb, Geoffrey, Butler, Douglas and Newlands, Douglas 2003, On detecting differences between groups, in KDD-2003 : proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : August 24-27, 2003, Washington, D.C., Association for Computing Machinery, New York, N.Y., pp. 256-265.

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Title On detecting differences between groups
Author(s) Webb, Geoffrey
Butler, Douglas
Newlands, Douglas
Conference name ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (9th : 2003 : Washington, D.C.)
Conference location Washington, D.C.
Conference dates 24-27 Aug. 2003
Title of proceedings KDD-2003 : proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining : August 24-27, 2003, Washington, D.C.
Editor(s) Domingos, Pedro
Faloutsos, Christos
Publication date 2003
Start page 256
End page 265
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Summary Understanding the differences between contrasting groups is a fundamental task in data analysis. This realization has led to the development of a new special purpose data mining technique, contrast-set mining. We undertook a study with a retail collaborator to compare contrast-set mining with existing rule-discovery techniques. To our surprise we ob- served that straightforward application of an existing commercial rule-discovery system, Magnum Opus, could successfully perform the contrast-set-mining task. This led to the realization that contrast-set mining is a special case of the more general rule-discovery task. We present the results of our study together with a proof of this conclusion.
ISBN 1581137370
9781581137378
Language eng
Field of Research 080605 Decision Support and Group Support Systems
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2003, ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30009573

Document type: Conference Paper
Collection: School of Information Technology
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