Correlation-Based Refinement of Rules with Numerical Attributes

Learning rules is a common way of extracting usefulinformation from knowledge or data bases. Many ofsuch data sets contain numerical attributes. However,approaches like ILP or association rule mining are optimizedfor data with categorical values, and consideringnumerical attributes is expensive. In this paper,we present an extension to the top-down ILP algorithm,which enables an efficient discovery of datalogrules from data with both numerical and categorical attributes.Our approach comprises a preprocessing phasefor computing the correlations between numerical andcategorical attributes, as well as an extension to the ILPrefinement step, which enables us to detect interestingcandidate rules and to suggest refinements with relevantattribute combinations. We report on experiments withU.S. Census data, Freebase and DBpedia, and show thatour approach helps to efficiently discover rules with numericalintervals.