Paper Info
Title
Reinventing the Contingency Wheel: Scalable Visual Analytics of Large Categorical Data
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.
Year
DOI
Venue
2012
10.1109/TVCG.2012.254
IEEE Trans. Vis. Comput. Graph.
Keywords
Field
DocType
data visualization,data visualisation,visual analytics,contingency table analysis,motion pictures,histograms
Computer vision,Data mining,Histogram,Data visualization,Computer science,Visualization,Categorical variable,Visual analytics,Contingency table,Artificial intelligence,Contingency,Scalability
Journal
Volume
Issue
ISSN
18
12
1077-2626
Citations 
PageRank 
References 
14
0.67
14
Authors
4
Name
Order
Citations
PageRank
Bilal Alsallakh119611.71
wolfgang aigner284251.72
Silvia Miksch32212174.85
M. Eduard Gröller456138.47