| Abstract: | Rare association rules correspond to rare, or infrequent, itemsets, as opposed
to frequent ones that are targeted by conventional pattern miners. Rare rules reflect regularities
of local, rather than global, scope that can nevertheless provide valuable insights
to an expert, especially in areas such as genetics and medical diagnosis where some specific
deviations/illnesses occur only in a small number of cases. The work presented here is motivated
by the long-standing open question of efficiently mining strong rare rules, i.e., rules
with high confidence and low support. We also propose an efficient solution for finding the
set of minimal rare itemsets. This set serves as a basis for generating rare association rules. |
