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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KDD-2001 : proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Provost, Foster Srikant, Ramakrishnan
Association for Computer Machinery
Place of publication
New York, NY
This paper further develops Aumann and Lindell's  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.
Field of Research
080109 Pattern Recognition and Data Mining
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences