Deep exploration of multidimensional data with linkable scatterplots
- Publication Type:
- Conference Proceeding
- Citation:
- ACM International Conference Proceeding Series, 2016, pp. 43 - 50
- Issue Date:
- 2016-09-24
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Vinci2016_NguyenSimoeffHuang.pdf | Published version | 1.77 MB |
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© 2016 ACM. Clarity, simplicity and visual adjustability to the preference of the analyst are key aspects of the visualization techniques required by visual analytics in broad sense. Scatterplots and scatterplot matrices are commonly used for visually analyzing multidimensional multivariate data. This paper presents a new approach for deep visual exploration of large multi-Attribute data using linkable scatterplots. Proposed method overcomes the limitations of the single scatterplot by providing more plot panels for better comparison while it reduces the unnecessary number of panels of the scatterplot matrix method. The panels are fully interactive and linking together where variables can be mapped on axes independently or on common visual attributes such as color, size and shape. We illustrate the effectiveness of proposed linkable scatterplot method on various data sets.
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