A Technique for Selection and Drawing of Scatterplots for Multi-Dimensional Data Visualization
Takayuki Itoh, Asuka Nakabayashi · 2019
Scatterplot matrix and parallel coordinate plots are well-used multi-dimensional data visualization techniques. These techniques have a problem that they need a very large screen space when an input dataset has an enormous number of dimensions. To solve this problem, we propose a method for selecting important scatterplots from all scatterplots generated from input datasets and for drawing the scatterplots as ”outliers” and ”regions enclosing non-outlier plots.” The technique is useful for users to determine whether to delete outliers from the datasets and form mathematical models of non-outlier plots. This paper introduces an example of visualization using this technique with a retail transaction dataset and climate values.