Exploring hierarchical multidimensional data with unified views of distribution and correlation

  • Authors:

  • Mark John Sifer;John Michael Potter

  • Affiliations:

  • School of Information Systems and Technology, University of Wollongong, Wollongong NSW 2522, Australia;School of Computer Science & Engineering, University of New South Wales, Sydney NSW 2052, Australia

  • Venue:
  • Journal of Visual Languages and Computing
  • Year:
  • 2013

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Abstract

Data analysts explore data by inspecting features such as clustering, distribution and correlation. Much existing research has focused on different visualisations for different data exploration tasks. For example, a data analyst might inspect clustering and correlation with scatterplots, but use histograms to inspect a distribution. Such visualisations allow an analyst to confirm prior expectations. For example, a scatterplot may confirm an expected correlation or may show deviations from the expected correlation. In order to better facilitate discovery of unexpected features in data, however, a combination of different perspectives may be needed. In this paper, we combine distributional and correlational views of hierarchical multidimensional data. Our unified view supports the simultaneous exploration of data distribution and correlation. By presenting a unified view, we aim to increase the chances of discovery of unexpected data features, and to provide the means to explore such features in detail. Further, our unified view is equipped with a small number of primitive interaction operators which a user composes to facilitate smooth and flexible exploration.