Standardizing Interestingness Measures for Association Rules
This work addresses a methodological issue in data mining for researchers and practitioners, but it is incremental as it extends prior standardization efforts from one measure to three.
The authors tackled the problem of interpreting interestingness measures for association rules by standardizing them to account for rule-specific constraints, deriving standardized versions of three measures and comparing them to raw versions and standardized lift using real and simulated data.
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.