Visual exploration of categorical and mixed data sets

Authors:
Sara Johansson
Affiliations:
Linköping University, Sweden
Venue:
Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration
Year:
2009

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Abstract

For categorical data there does not exist any similarity measure which is as straight forward and general as the numerical distance between numerical items. Due to this it is often difficult to analyse data sets including categorical variables or a combination of categorical and numerical variables (mixed data sets). 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 [16]. 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 effectiveness of the quantification process and of the features of the application is demonstrated through a case scenario.