  Interestingness measures provide information that can be used to prune or
select association rules. A given value of an interestingness measure is often
interpreted relative to the overall range of the values that the
interestingness measure can take. However, properties of individual association
rules restrict the values an interestingness measure can achieve. An
interesting measure can be standardized to take this into account, but this has
only been done for one interestingness measure to date, i.e., the lift.
Standardization provides greater insight than the raw value and may even alter
researchers' perception of the data. We derive standardized analogues of three
interestingness measures and use real and simulated data to compare them to
their raw versions, each other, and the standardized lift.
