document: Mining rank-correlated sets of numerical attributes We study the mining of interesting patterns in the presence of numerical attributes . Instead of the usual discretization methods , we propose the use of rank based measures to score the similarity of sets of numerical attributes . New support measures for numerical data are introduced , based on extensions of Kendall 's tau , and Spearman 's Footrule and rho . We show how these support measures are related . Furthermore , we introduce a novel type of pattern combining numerical and categorical attributes . We give efficient algorithms to find all frequent patterns for the proposed support measures , and evaluate their performance on real-life datasets .
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