Visual exploration of categorical and mixed data sets
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
A Tool for Analyzing Categorical Data Visually with Granular Representation
Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009
Visual analysis of mixed data sets using interactive quantification
ACM SIGKDD Explorations Newsletter
Clustering of heterogeneously typed data with soft computing - a case study
MICAI'11 Proceedings of the 10th international conference on Artificial Intelligence: advances in Soft Computing - Volume Part II
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Data sets containing a combination of categorical and continuous variables (mixed data sets) are difficult to analyse since no generalized similarity measure exists for categorical variables. Quantification of categorical variables makes it possible to represent this type of data using techniques designed for numerical data. This paper presents a quantification process of categorical variables in mixed data sets that incorporates information on relationships among the continuous variables into the process, as well as utilizing the domain knowledge of a user. An interactive visualization environment using parallel coordinates as a visual interface is provided, where the user is able to control the quantification process and analyse the result. The efficiency of the approach is demonstrated using two mixed data sets.