G. Webb, and S. Zhang. Data Mining and Knowledge Discovery, 10 (1):
39-79(2005)
Abstract
K-most-interesting rule discovery finds the k rules that optimize a user-specified measure of interestingness with respect to a set of sample data and user-specified constraints. This approach avoids many limitations of the frequent itemset approach of association rule discovery. This paper presents a scalable algorithm applicable to a wide range of k-most-interesting rule discovery tasks and demonstrates its efficiency.
%0 Journal Article
%1 WebbZhang05
%A Webb, G. I.
%A Zhang, S.
%C Netherlands
%D 2005
%I Springer
%J Data Mining and Knowledge Discovery
%K Association Discovery, OPUS Rule discovery, sound statistically
%N 1
%P 39-79
%T k-Optimal-Rule-Discovery
%V 10
%X K-most-interesting rule discovery finds the k rules that optimize a user-specified measure of interestingness with respect to a set of sample data and user-specified constraints. This approach avoids many limitations of the frequent itemset approach of association rule discovery. This paper presents a scalable algorithm applicable to a wide range of k-most-interesting rule discovery tasks and demonstrates its efficiency.
@article{WebbZhang05,
abstract = {K-most-interesting rule discovery finds the k rules that optimize a user-specified measure of interestingness with respect to a set of sample data and user-specified constraints. This approach avoids many limitations of the frequent itemset approach of association rule discovery. This paper presents a scalable algorithm applicable to a wide range of k-most-interesting rule discovery tasks and demonstrates its efficiency.},
added-at = {2016-03-20T05:42:04.000+0100},
address = {Netherlands},
author = {Webb, G. I. and Zhang, S.},
biburl = {https://www.bibsonomy.org/bibtex/2ba962d7deda58efa2a7fda211e575363/giwebb},
interhash = {f7cf385d4b2380d566612cd86a21715e},
intrahash = {ba962d7deda58efa2a7fda211e575363},
journal = {Data Mining and Knowledge Discovery},
keywords = {Association Discovery, OPUS Rule discovery, sound statistically},
number = 1,
pages = {39-79},
publisher = {Springer},
timestamp = {2016-03-20T05:42:04.000+0100},
title = {k-Optimal-Rule-Discovery},
volume = 10,
year = 2005
}