Visual-Based Detection of Properties of Confirmation Measures
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.
KeywordsVisualization interestingness measures confirmation measures properties of measures
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- 4.Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Computing Surveys 38(3) (2006)Google Scholar
- 10.Piatetsky-Shapiro, G.: Discovery, analysis, and presentation of strong rules. In: Knowledge Discovery in Databases, pp. 229–248. AAAI/MIT Press (1991)Google Scholar
- 11.Susmaga, R., Szczęch, I.: Visualization of interestingness measures. In: Proceedings of the 6th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, pp. 95–99 (2013)Google Scholar