G. Webb. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)short paper, page 383-388. New York, The Association for Computing Machinery, (2001)
Abstract
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.
%0 Conference Paper
%1 Webb01a
%A Webb, G. I.
%B Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)short paper
%C New York
%D 2001
%E Provost, F.
%E Srikant, R.
%I The Association for Computing Machinery
%K Association Discovery Impact OPUS, Rule rules,
%P 383-388
%T Discovering Associations with Numeric Variables
%X 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.
@inproceedings{Webb01a,
abstract = {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.},
added-at = {2016-03-20T05:42:04.000+0100},
address = {New York},
audit-trail = {*},
author = {Webb, G. I.},
biburl = {https://www.bibsonomy.org/bibtex/200a636e03aa31397423a1ccdd15bedd8/giwebb},
booktitle = {Proceedings of the Seventh {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining (KDD-2001)[short paper]},
editor = {Provost, F. and Srikant, R.},
interhash = {9a10f2404bbe7eb7170c46e79e457ae5},
intrahash = {00a636e03aa31397423a1ccdd15bedd8},
keywords = {Association Discovery Impact OPUS, Rule rules,},
location = {San Francisco, CA},
pages = {383-388},
publisher = {The Association for Computing Machinery},
timestamp = {2016-03-20T05:42:04.000+0100},
title = {Discovering Associations with Numeric Variables},
year = 2001
}