Using sunflower metaphor to reduce clutter of scatterplot
Congmin Li, Jie Li · 2017
We present a new metaphor-based approach to exploring the scatterplot consisting of huge amount of points. To reduce clutter, we group the data points having similar attribute distributions together and utilize the sunflower metaphor to represent the points of one group, thus reducing the original data points to several sunflowers. With this approach, the user is able to detect stable states, recurring states, outlier points, and gain knowledge about the transitions between different states. The components of our approach are normalization, grouping, dimensionality reduction and visualization. The effectiveness of the approach is shown by applying it to an actual dataset.