Reinventing the Contingency Wheel: Scalable Visual Analytics of Large Categorical Data

Bilal Alsallakh, W Aigner, Silvia Miksch, Meister Eduard Gröller
Reinventing the Contingency Wheel: Scalable Visual Analytics of Large Categorical Data
IEEE Transaction on Visualization and Computer Graphics, 18(12):2849-2858, December 2012.

Information

Abstract

Contingency tables summarize the relations between categorical variables and arise in both scientific and business domains. Asymmetrically large two-way contingency tables pose a problem for common visualization methods. The Contingency Wheel has been recently proposed as an interactive visual method to explore and analyze such tables. However, the scalability and readability of this method are limited when dealing with large and dense tables. In this paper we present Contingency Wheel++, new visual analytics methods that overcome these major shortcomings: (1) regarding automated methods, a measure of association based on Pearson's residuals alleviates the bias of the raw residuals originally used, (2) regarding visualization methods, a frequency-based abstraction of the visual elements eliminates overlapping and makes analyzing both positive and negative associations possible, and (3) regarding the interactive exploration environment, a multi-level overview+detail interface enables exploring individual data items that are aggregated in the visualization or in the table using coordinated views. We illustrate the applicability of these new methods with a use case and show how they enable discovering and analyzing nontrivial patterns and associations in large categorical data.

Additional Files and Images

No additional files or images.

Weblinks

BibTeX

@article{Alsallakh_2012_RCW,
  title =      "Reinventing the Contingency Wheel: Scalable Visual Analytics
               of Large Categorical Data",
  author =     "Bilal Alsallakh and W Aigner and Silvia Miksch and Meister
               Eduard Gr{"o}ller",
  year =       "2012",
  abstract =   "Contingency tables summarize the relations between
               categorical variables and arise in both scientific and
               business domains. Asymmetrically large two-way contingency
               tables pose a problem for common visualization methods. The
               Contingency Wheel has been recently proposed as an
               interactive visual method to explore and analyze such
               tables. However, the scalability and readability of this
               method are limited when dealing with large and dense tables.
               In this paper we present Contingency Wheel++, new visual
               analytics methods that overcome these major shortcomings:
               (1) regarding automated methods, a measure of association
               based on Pearson's residuals alleviates the bias of the raw
               residuals originally used, (2) regarding visualization
               methods, a frequency-based abstraction of the visual
               elements eliminates overlapping and makes analyzing both
               positive and negative associations possible, and (3)
               regarding the interactive exploration environment, a
               multi-level overview+detail interface enables exploring
               individual data items that are aggregated in the
               visualization or in the table using coordinated views. We
               illustrate the applicability of these new methods with a use
               case and show how they enable discovering and analyzing
               nontrivial patterns and associations in large categorical
               data.",
  month =      dec,
  journal =    "IEEE Transaction on Visualization and Computer Graphics",
  number =     "12",
  volume =     "18",
  pages =      "2849--2858",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2012/Alsallakh_2012_RCW/",
}