G. Webb. Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000), page 99-107. New York, The Association for Computing Machinery, (2000)
Abstract
This paper argues that for some applications direct search for association rules can be more efficient than the two stage process of the Apriori algorithm which first finds large item sets which are then used to identify associations. In particular, it is argued, Apriori can impose large computational overheads when the number of frequent itemsets is very large. This will often be the case when association rule analysis is performed on domains other than basket analysis or when it is performed for basket analysis with basket information augmented by other customer information. An algorithm is presented that is computationally efficient for association rule analysis during which the number of rules to be found can be constrained and all data can be maintained in memory.
%0 Conference Paper
%1 Webb00b
%A Webb, G. I.
%B Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000)
%C New York
%D 2000
%E Ramakrishnan, R.
%E Stolfo, S.
%I The Association for Computing Machinery
%K Association Discovery OPUS, Rule Search,
%P 99-107
%T Efficient Search for Association Rules
%X This paper argues that for some applications direct search for association rules can be more efficient than the two stage process of the Apriori algorithm which first finds large item sets which are then used to identify associations. In particular, it is argued, Apriori can impose large computational overheads when the number of frequent itemsets is very large. This will often be the case when association rule analysis is performed on domains other than basket analysis or when it is performed for basket analysis with basket information augmented by other customer information. An algorithm is presented that is computationally efficient for association rule analysis during which the number of rules to be found can be constrained and all data can be maintained in memory.
@inproceedings{Webb00b,
abstract = {This paper argues that for some applications direct search for association rules can be more efficient than the two stage process of the Apriori algorithm which first finds large item sets which are then used to identify associations. In particular, it is argued, Apriori can impose large computational overheads when the number of frequent itemsets is very large. This will often be the case when association rule analysis is performed on domains other than basket analysis or when it is performed for basket analysis with basket information augmented by other customer information. An algorithm is presented that is computationally efficient for association rule analysis during which the number of rules to be found can be constrained and all data can be maintained in memory.},
added-at = {2016-03-20T05:42:04.000+0100},
address = {New York},
audit-trail = {*},
author = {Webb, G. I.},
biburl = {https://www.bibsonomy.org/bibtex/25a12f4db5a718172720858442edbf8e9/giwebb},
booktitle = {Proceedings of the Sixth {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining (KDD-2000)},
editor = {Ramakrishnan, R. and Stolfo, S.},
interhash = {7e2fcdea1aca9eaff26c62104fe843d1},
intrahash = {5a12f4db5a718172720858442edbf8e9},
keywords = {Association Discovery OPUS, Rule Search,},
location = {Boston, MA},
pages = {99-107},
publisher = {The Association for Computing Machinery},
timestamp = {2016-03-20T05:42:04.000+0100},
title = {Efficient Search for Association Rules},
year = 2000
}