Mining rank-correlated sets of numerical attributes

Abstract

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.