Technometrics
CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Guidelines for using multiple views in information visualization
AVI '00 Proceedings of the working conference on Advanced visual interfaces
Interactive data visualization using focusing and linking
VIS '91 Proceedings of the 2nd conference on Visualization '91
XmdvTool: integrating multiple methods for visualizing multivariate data
VIS '94 Proceedings of the conference on Visualization '94
Mapping nominal values to numbers for effective visualization
Information Visualization - Special issue of selected and extended InfoVis 03 papers
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Clustering For Data Mining: A Data Recovery Approach (Chapman & Hall/Crc Computer Science)
Parallel Sets: Interactive Exploration and Visual Analysis of Categorical Data
IEEE Transactions on Visualization and Computer Graphics
State of the Art: Coordinated & Multiple Views in Exploratory Visualization
CMV '07 Proceedings of the Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization
The GAV Toolkit for Multiple Linked Views
CMV '07 Proceedings of the Fifth International Conference on Coordinated and Multiple Views in Exploratory Visualization
Interactive Quantification of Categorical Variables in Mixed Data Sets
IV '08 Proceedings of the 2008 12th International Conference Information Visualisation
IEEE Symposium on Visual Analytics Science and Technology 2007
VAST '07 Proceedings of the 2007 IEEE Symposium on Visual Analytics Science and Technology
Interactive spatio-temporal cluster analysis of VAST challenge 2008 datasets
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
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It is often difficult to analyse data sets including a combination of categorical and numerical variables (mixed data sets) since there does not exist any similarity measure which is as straight forward and general as the numerical distance between numerical items. Quantification of categorical variables enables analysis using commonly used visual representations and analysis techniques for numerical data. This paper presents a tool for exploratory analysis of categorical and mixed data which uses a quantification process introduced in [17]. The application enables analysis of mixed data sets by providing an environment for exploratory analysis using common visual representations in multiple coordinated views and algorithmic analysis that facilitates detection of potentially interesting patterns within combinations of categorical and numerical variables. The generality and usefulness of the quantification process and of the features of the application is demonstrated through a case scenario using a data set from the IEEE VAST 2008 Challenge [13].