Editing Association rule learning (section)

=== Alternative measures of interestingness ===
In addition to confidence, other measures of ''interestingness'' for rules have been proposed. Some popular measures are:

* All-confidence<ref name="allconfidence">{{cite journal |doi=10.1109/TKDE.2003.1161582 |title=Alternative interest measures for mining associations in databases |journal=IEEE Transactions on Knowledge and Data Engineering |volume=15 |pages=57–69 |year=2003 |last1=Omiecinski |first1=E.R. |citeseerx=10.1.1.329.5344 |s2cid=18364249 }}</ref>
* Collective strength<ref name="collectivestrength">{{cite book |doi=10.1145/275487.275490 |chapter=A new framework for itemset generation |title=Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems - PODS '98 |pages=18–24 |year=1998 |last1=Aggarwal |first1=Charu C. |last2=Yu |first2=Philip S. |isbn=978-0897919968 |citeseerx=10.1.1.24.714 |s2cid=11934586 }}</ref>
* Leverage<ref name="leverage">Piatetsky-Shapiro, Gregory; ''Discovery, analysis, and presentation of strong rules'', Knowledge Discovery in Databases, 1991, pp. 229-248</ref>

Several more measures are presented and compared by Tan et al.<ref name="measurescomp">{{cite journal |doi=10.1016/S0306-4379(03)00072-3 |title=Selecting the right objective measure for association analysis |journal=Information Systems |volume=29 |issue=4 |pages=293–313 |year=2004 |last1=Tan |first1=Pang-Ning |last2=Kumar |first2=Vipin |last3=Srivastava |first3=Jaideep |citeseerx=10.1.1.331.4740 }}</ref> and by Hahsler.<ref name="michael.hahsler.net">Michael Hahsler (2015). A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules. https://mhahsler.github.io/arules/docs/measures</ref> Looking for techniques that can model what the user has known (and using these models as interestingness measures) is currently an active research trend under the name of "Subjective Interestingness."