Visual-based detection of properties of confirmation measures

Abstract

The paper presents a visualization technique that facilitates and eases analyses of interestingness measures with respect to their properties. Detection of properties possessed by these measures is especially important when choosing a measure for KDD tasks. Our visual-based approach is a useful alternative to often laborious and time consuming theoretical studies, as it allows to promptly perceive properties of the visualized measures. Assuming a common, four-dimensional domain of the measures, a synthetic dataset consisting of all possible contingency tables with the same number of observations is generated. It is then visualized in 3D using a tetrahedron-based barycentric coordinate system. Additional scalar function - an interestingness measure - is rendered using colour. To demonstrate the capabilities of the proposed technique, we detect properties of a particular group of measures, known as confirmation measures. © 2014 Springer International Publishing.