Abstract
The lift of an association rule is frequently used, both in itself and as a component in formulae, to gauge the interestingness of a rule. The range of values that lift may take is used to standardise lift so that it is more effective as a measure of interestingness. This standardisation is extended to account for minimum support and confidence thresholds. A method of visualising standardised lift, through the relationship between lift and its upper and lower bounds, is proposed. The application of standardised lift as a measure of interestingness is demonstrated on college application data and social questionnaire data. In the latter case, negations are introduced into the mining paradigm and an argument for this inclusion is put forward. This argument includes a quantification of the number of extra rules that arise when negations are considered.
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