DSPCP: A Data Scalable Approach for Identifying Relationships in Parallel Coordinates

Parallel coordinates plots (PCPs) are a well-studied technique for exploring multi-attribute datasets. In many situations, users find them a flexible method to analyze and interact with data. Unfortunately, using PCPs becomes challenging as the number of data items grows large or multiple trends within the data mix in the visualization. The resulting overdraw can obscure important features. A number of modifications to PCPs have been proposed, including using color, opacity, smooth curves, frequency, density, and animation to mitigate this problem. However, these modified PCPs tend to have their own limitations in the kinds of relationships they emphasize. We propose a new data scalable design for representing and exploring data relationships in PCPs. The approach exploits the point/line duality property of PCPs and a local linear assumption of data to extract and represent relationship summarizations. This approach simultaneously shows relationships in the data and the consistency of those relationships. Our approach supports various visualization tasks, including mixed linear and nonlinear pattern identification, noise detection, and outlier detection, all in large data. We demonstrate these tasks on multiple synthetic and real-world datasets.

Hoa Nguyen, and Paul Rosen. DSPCP: A Data Scalable Approach for Identifying Relationships in Parallel Coordinates. IEEE Transactions on Visualization and Computer Graphics (TVCG), 2017.

@article{nguyen2017dspcp,
  title = {{DSPCP}: A Data Scalable Approach for Identifying Relationships in Parallel
    Coordinates},
  author = {Nguyen, Hoa and Rosen, Paul},
  journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},
  volume = {24},
  pages = {1301--1315},
  year = {2017},
  note = {textit{Presented at IEEE SciVis 2017.}},
  abstract = {Parallel coordinates plots (PCPs) are a well-studied technique for
    exploring multi-attribute datasets. In many situations, users find them a flexible
    method to analyze and interact with data. Unfortunately, using PCPs becomes challenging
    as the number of data items grows large or multiple trends within the data mix in the
    visualization. The resulting overdraw can obscure important features. A number of
    modifications to PCPs have been proposed, including using color, opacity, smooth curves,
    frequency, density, and animation to mitigate this problem. However, these modified PCPs
    tend to have their own limitations in the kinds of relationships they emphasize. We
    propose a new data scalable design for representing and exploring data relationships in
    PCPs. The approach exploits the point/line duality property of PCPs and a local linear
    assumption of data to extract and represent relationship summarizations. This approach
    simultaneously shows relationships in the data and the consistency of those
    relationships. Our approach supports various visualization tasks, including mixed linear
    and nonlinear pattern identification, noise detection, and outlier detection, all in
    large data. We demonstrate these tasks on multiple synthetic and real-world datasets.}
}