Applied multivariate statistical analysis
Applied multivariate statistical analysis
Fast ordering of large categorical datasets for better visualization
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGKDD Explorations Newsletter
SPSS Categories 10.0
Similarity Clustering of Dimensions for an Enhanced Visualization of Multidimensional Data
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Exploring N-dimensional databases
VIS '90 Proceedings of the 1st conference on Visualization '90
Parallel coordinates: a tool for visualizing multi-dimensional geometry
VIS '90 Proceedings of the 1st conference on Visualization '90
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
Bioinformatic Insights from Metagenomics through Visualization
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Parallel Sets: Interactive Exploration and Visual Analysis of Categorical Data
IEEE Transactions on Visualization and Computer Graphics
Discussion: Interacting with parallel coordinates
Interacting with Computers
High-dimensional data visualisation: The textile plot
Computational Statistics & Data Analysis
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
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Data sets with a large number of nominal variables, some with high cardinality, are becoming increasingly common and need to be explored. Unfortunately, most existing visual exploration displays are designed to handle numeric variables only. When importing data sets with nominal values into such visualization tools, most solutions to date are rather simplistic. Often, techniques that map nominal values to numbers do not assign order or spacing among the values in a manner that conveys semantic relationships. Moreover, displays designed for nominal variables usually cannot handle high cardinality variables well. This paper addresses the problem of how to display nominal variables in general-purpose visual exploration tools designed for numeric variables. Specifically, we investigate (1) how to assign order and spacing among the nominal values, and (2) how to reduce the number of distinct values to display. We propose that nominal variables be pre-processed using a Distance-Quantification-Classing (DQC) approach before being imported into a visual exploration tool. In the Distance Step, we identify a set of independent dimensions that can be used to calculate the distance between nominal values. In the Quantification Step, we use the independent dimensions and the distance information to assign order and spacing among the nominal values. In the Classing Step, we use results from the previous steps to determine which values within a variable are similar to each other and thus can be grouped together. Each step in the DQC approach can be accomplished by a variety of techniques. We extended the XmdvTool package to incorporate this approach. We evaluated our approach on several data sets using a variety of evaluation measures.