Association rule learning may discover that customers that buy onions and potatoes together are likely to also purchase hamburger meat.

Classification may discover that customers that bought onions, potatoes, and hamburger meats were purchasing items for a cookout.

Contrast set learning may discover that the major difference between customers shopping for a cookout and those shopping for an anniversary dinner are that customers acquiring items for a cookout purchase onions, potatoes, and hamburger meat (and do not purchase foie gras or champagne).

Rather than seeking the differences between all groups, treatment learning specifies a particular group to focus on, applies a weight to this desired grouping, and lumps the remaining groups into one "undesired" category.

Treatment learning has a stated focus on minimal theories. In practice, treatment are limited to a maximum of four constraints (i.e., rather than stating all of the reasons that a rocket differs from a skateboard, a treatment learner will state one to four major differences that predict for rockets at a high level of statistical significance).

The minimum deviation size: The maximum difference between the support of any two groups must be greater than a user-specified threshold.

Expected cell frequencies: The expected cell frequencies of a contingency table can only decrease as the contrast set is specialized. When these frequencies are too small, the validity of the chi-square test is violated.

${\displaystyle \chi ^{2}}$ bounds: An upper bound is kept on the distribution of a statistic calculated when the null hypothesis is true. Nodes are pruned when it is no longer possible to meet this cutoff.