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Multidimensional data visualization applying a variety-oriented scatterplot selection technique

Abstract

Multidimensional data visualization is one of the most active research topics in information visualization since various information in our daily life forms multidimensional datasets. Scatterplot selection is an effective approach to represent essential portions of multidimensional data in a limited display space. Various metrics for evaluating scatterplots, such as scagnostics, have been applied to scatterplot selection. One of the open problems of this research topic is that various scatterplots cannot be selected if we simply apply one of the metrics. In other words, we may want to apply multiple metrics simultaneously in a balanced manner when we want to select a variety of scatterplots. This paper presents a new scatterplot selection technique that solves this problem. First, the technique calculates the scores of scatterplots with multiple metrics and then constructs a graph by connecting pairs of scatterplots that have similar scores. Next, it uses a graph coloring algorithm to assign different colors to scatterplots that have similar scores. We can extract a set of various scatterplots by selecting them that the specific same color is assigned. This paper introduces two case studies: the former study is with a retail transaction dataset while the latter study is with a design optimization dataset.

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Acknowledgements

We appreciate ABEJA, Inc. for providing the retail transaction dataset. Also, we appreciate Prof. Shigeru Obayashi of Tohoku University, Japan, for providing the design optimization dataset.

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Correspondence to Takayuki Itoh.

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Itoh, T., Nakabayashi, A. & Hagita, M. Multidimensional data visualization applying a variety-oriented scatterplot selection technique. J Vis (2022). https://doi.org/10.1007/s12650-022-00871-6

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  • DOI: https://doi.org/10.1007/s12650-022-00871-6

Keywords

  • Multidimensional data visualization
  • Scatterplot selection
  • Graph coloring algorithm
  • Retail transaction data
  • Design optimization data