• Corpus ID: 204796265

Sampling in Multiview and Multi-class Scatterplots via Set Cover Optimization

@inproceedings{Hu2019SamplingIM,
  title={Sampling in Multiview and Multi-class Scatterplots via Set Cover Optimization},
  author={Ruizhen Hu and Tingkai Sha and Oliver Matias van Kaick and Oliver Deussen and Hui Huang},
  year={2019}
}
We present a method for data sampling in scatterplots by jointly optimizing point selection for different views or classes. Our method uses space-filling curves (Z-order curves) that partition a point set into subsets that, when covered each by one sample, provide a sampling or coreset with good approximation guarantees in relation to the original point set. For scatterplot matrices with multiple views, different views provide different space-filling curves, leading to different partitions of… 

References

SHOWING 1-10 OF 42 REFERENCES

Optimizing Color Assignment for Perception of Class Separability in Multiclass Scatterplots

An effective approach for color assignment based on a set of given colors that is designed to optimize the perception of scatterplots is presented and is able to support users in distinguishing cluster numbers faster and more precisely than default assignment methods.

Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices

It is revealed that 2D scatterplots are often 'good enough', that is, neither SPLOM nor interactive 3D adds notably more cluster separability with the chosen DR technique.

Generalized Scatter Plots

The generalized scatter plot technique is proposed, which allows an overlap-free representation of large data sets to fit entirely into the display, and an optimization function that takes overlap and distortion of the visualization into acccount is identified.

Selecting good views of high‐dimensional data using class consistency

This paper proposes two quantitative measures of class consistency, one based on the distance to the class's center of gravity, and another basedOn the entropies of the spatial distributions of classes, both of which are efficient and robust.

Multi-class blue noise sampling

  • Li-yi Wei
  • Computer Science
    ACM Trans. Graph.
  • 2010
This work extends blue noise sampling to multiple classes where each individual class as well as their unions exhibit blue noise characteristics, and proposes two flavors of algorithms to generate such multi-class blue noise samples.

Splatterplots: Overcoming Overdraw in Scatter Plots

It is shown how Splatterplots can be an effective alternative to traditional methods of displaying scatter data communicating data trends, outliers, and data set relationships much like traditional scatter plots, but scaling to data sets of higher density and up to millions of points on the screen.

Continuous Scatterplots

Since continuous scatterplots do not only sample data at grid points but interpolate data values within cells, a dense and complete visualization of the data set is achieved that scales well with increasing data set size.

By chance is not enough: preserving relative density through nonuniform sampling

  • E. BertiniG. Santucci
  • Computer Science
    Proceedings. Eighth International Conference on Information Visualisation, 2004. IV 2004.
  • 2004
This work focuses on 2D scatter-plots, devising a novel non uniform data sampling strategy able to preserve in an effective way relative densities, and defines some metrics able to characterize the image decay.

Give Chance a Chance: Modeling Density to Enhance Scatter Plot Quality through Random Data Sampling

This paper focuses on 2D scatter plots, proposing a ‘feature preservation’ approach, based on the idea of modeling the visualization in a virtual space in order to analyze its features (e.g., absolute density, relative density, etc.).

Dynamic Opacity Optimization for Scatter Plots

This work presents a user-driven model of opacity scaling for scatter plots built from crowd-sourced responses to opacity scaling tasks using several synthetic data distributions, and then test the model on a collection of real-world data sets.