Editing Association rule learning (section)

==== OPUS search ====

OPUS is an efficient algorithm for rule discovery that, in contrast to most alternatives, does not require either monotone or anti-monotone constraints such as minimum support.<ref name=OPUS>Webb, Geoffrey I. (1995); ''OPUS: An Efficient Admissible Algorithm for Unordered Search'', Journal of Artificial Intelligence Research 3, Menlo Park, CA: AAAI Press, pp. 431-465 [http://webarchive.loc.gov/all/20011118141304/http://www.cs.washington.edu/research/jair/abstracts/webb95a.html online access]</ref> Initially used to find rules for a fixed consequent<ref name="OPUS" /><ref name="Bayardo">{{Cite journal |doi=10.1023/A:1009895914772 |last1=Bayardo |first1=Roberto J. Jr. |last2=Agrawal |first2=Rakesh |last3=Gunopulos |first3=Dimitrios |year=2000 |title=Constraint-based rule mining in large, dense databases |journal=Data Mining and Knowledge Discovery |volume=4 |issue=2 |pages=217–240 |s2cid=5120441 }}</ref> it has subsequently been extended to find rules with any item as a consequent.<ref name="webb">{{cite book |doi=10.1145/347090.347112 |chapter=Efficient search for association rules |title=Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '00 |pages=99–107 |year=2000 |last1=Webb |first1=Geoffrey I. |isbn=978-1581132335 |citeseerx=10.1.1.33.1309 |s2cid=5444097 }}</ref> OPUS search is the core technology in the popular Magnum Opus association discovery system.