Visual Exploration Of Multiway Dependencies In Multivariate Data

Analyzing dependencies among variables within multivariate data is an important and challenging problem, especially when the number of data points is large, the number of variables is high, or multiway dependencies are of interest. Several visualization methods have been proposed to aid in the exploration of such information through the direct visualization of the summary statistics. These methods are typically limited to the study of all possible pairwise relationship but in a manner that does not scale to large multidimensional data. In cases where $3$-way relationships are investigated, only subsets of dimensions are considered. In this paper, we propose a novel technique for analyzing multiway dependencies through an overview+detail visualization. In this approach, the overview represents all pairwise, $3$-, and $4$-way dependencies in the data using glyphs that provide a global visual exploration interface for selecting candidate relationships. Exploration is supported through interactive filtering, sorting, zooming, and selection operations. Once selected, the detailed view helps in developing an inference by providing specific information about those selected variables. Various use cases demonstrate how our approach helps to explore multiway dependencies efficiently in large datasets.

Hoa Nguyen, Paul Rosen, and Bei Wang. Visual Exploration Of Multiway Dependencies In Multivariate Data. ACM SIGGRAPH ASIA Symposium on Visualization, 2016.

@inproceedings{nguyen2016visual,
  title = {Visual Exploration of Multiway Dependencies in Multivariate Data},
  author = {Nguyen, Hoa and Rosen, Paul and Wang, Bei},
  booktitle = {ACM SIGGRAPH ASIA Symposium on Visualization},
  year = {2016},
  abstract = {Analyzing dependencies among variables within multivariate data is an
    important and challenging problem, especially when the number of data points is large,
    the number of variables is high, or multiway dependencies are of interest. Several
    visualization methods have been proposed to aid in the exploration of such information
    through the direct visualization of the summary statistics. These methods are typically
    limited to the study of all possible pairwise relationship but in a manner that does not
    scale to large multidimensional data. In cases where $3$-way relationships are
    investigated, only subsets of dimensions are considered. In this paper, we propose a
    novel technique for analyzing multiway dependencies through an overview+detail
    visualization. In this approach, the overview represents all pairwise, $3$-, and $4$-way
    dependencies in the data using glyphs that provide a global visual exploration interface
    for selecting candidate relationships. Exploration is supported through interactive
    filtering, sorting, zooming, and selection operations. Once selected, the detailed view
    helps in developing an inference by providing specific information about those selected
    variables. Various use cases demonstrate how our approach helps to explore multiway
    dependencies efficiently in large datasets. }
}