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