Abstract
Visualisation is usually one of the first steps in handling any data analysis problem. Visualisations are an intuitive way to discover inconsistencies, outliers, dependencies, interesting patterns and peculiarities in the data. However, due to modern computer technology, a vast number of visualisation techniques is available nowadays. Even if only simple scatterplots, plotting pairs of variables against each other, are considered, the number of scatterplots is too large for high-dimensional data to visually inspect each scatterplot. In this paper, we propose a system architecture called AVEDA (Automatic Visual Exploratory Data Analysis) which computes a large number of visualisations, filters out those ones that might contain special patterns and shows only these interesting visualisations to the user. The filtering process for the visualisations is based on statistical tests and statistical measures.
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Tschumitschew, K., Klawonn, F. (2009). AVEDA: Statistical Tests for Finding Interesting Visualisations. In: Velásquez, J.D., Ríos, S.A., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2009. Lecture Notes in Computer Science(), vol 5711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04595-0_29
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DOI: https://doi.org/10.1007/978-3-642-04595-0_29
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