Standardizing Interestingness Measures for Association Rules

TL;DR

This paper introduces standardized versions of three interestingness measures for association rules, accounting for rule-specific properties, and demonstrates their advantages over raw measures through experiments with real and simulated data.

Contribution

It extends the standardization approach to multiple interestingness measures beyond lift, providing more insightful and comparable rule evaluation.

Findings

Standardized measures offer better interpretability than raw values.

Standardization can change the perceived importance of rules.

The new measures outperform raw versions in experiments.

Abstract

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.