A General Measure of Rule Interestingness

The paper presents a new general measure of rule interestingness. Many known measures such as chi-square, gini gain or entropy gain can be obtained from this measure by setting some numerical parameters, including the amount of trust we have in the estimation of the probability distribution of the data. Moreover, we show that there is a continuum of measures having chi-square, Gini gain and entropy gain as boundary cases. Therefore our measure generalizes both conditional and unconditional classical measures of interestingness. Properties and experimental evaluation of the new measure are also presented.

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