Association Rule Mining (ARM) obtains a set of rules which
indicate that the consequent of a rule is likely to apply if the
antecedent applies.[1] To generate such rules, the first step is to
determine the support for sets of items (I) that may be present
in the data set, i.e., the frequency with which each combination
of items occurs. After eliminating those I for which the support
fails to meet a given minimum support threshold, the remaining
large I can be used to produce ARs of the form A B, where
A and B are disjoint subsets of a large I. The ARs generated are
usually pruned according to some notion of confidenc