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Bertjan Broeksema, Alexandru C. Telea and Thomas Baudel Visual Analysis of Multi-Dimensional Categorical Data Sets Computer Graphics Forum 32

Article first published online: 21 OCT 2013 | DOI: 10.1111/cgf.12194

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We present a set of interactive techniques for the visual analysis of multidimensional categorical data. Our approach is based on Multiple Correspondence Analysis (MCA), which allows one to analyze relationships, patterns, trends and outliers among dependent categorical variables. We use MCA as a dimensionality reduction technique to project both observations and their attributes in the same 2D space. We use a treeview to show attributes and their domains, a histogram of their representativity in the data set, and as a compact overview of attribute-related facts. A second view shows both attributes and observations.

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