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Discovering associations with numeric variables

Webb, Geoffrey 2001, Discovering associations with numeric variables, in KDD-2001 : proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computer Machinery, New York, NY, pp. 383-388.

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Title Discovering associations with numeric variables
Author(s) Webb, Geoffrey
Conference name International Conference on Knowledge Discovery and Data Mining (7th : 2001 : San Francisco, CA)
Conference location San Francisco, CA
Conference dates 26-29 Aug. 2001
Title of proceedings KDD-2001 : proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Editor(s) Provost, Foster
Srikant, Ramakrishnan
Publication date 2001
Start page 383
End page 388
Publisher Association for Computer Machinery
Place of publication New York, NY
Summary This paper further develops Aumann and Lindell's [3] proposal for a variant of association rules for which the consequent is a numeric variable. It is argued that these rules can discover useful interactions with numeric data that cannot be discovered directly using traditional association rules with discretization. Alternative measures for identifying interesting rules are proposed. Efficient algorithms are presented that enable these rules to be discovered for dense data sets for which application of Auman and Lindell's algorithm is infeasible.
ISBN 158113391X
9781581133912
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004447

Document type: Conference Paper
Collection: School of Information Technology
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