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

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.

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Citation

Hoa Nguyen and Paul Rosen. Dspcp: A data scalable approach for identifying relationships in parallel coordinates. IEEE Transactions on Visualization and Computer Graphics, 2017.

Bibtex


@article{Nguyen.2017.TVCG,
  title = {DSPCP: A Data Scalable Approach for Identifying Relationships in Parallel Coordinates},
  author = {Hoa Nguyen and Paul Rosen},
  journal = {IEEE Transactions on Visualization and Computer Graphics},
  year = {2017},
  publisher = {IEEE},
  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.}
}