Abstract
This paper addresses the problem of visualizing data of dimension higher than three. The method described is primarily aimed at so-called categorical (or nominal) data, but also applies to other discrete-valued data, i.e. data entities defined in terms of vectors of discrete attribute values. Categorical data is data with unordered attribute values, mostly because they are symbolic. Data with boolean values constitute a special case.
The method described utilizes the fact that data entities can be regarded as points in n-dimensional space, where n is the number of attributes recorded for each data entity. Although it is not possible to depict an n-dimensional cube using three dimensional graphics, it is possible to provide the user with an abstraction of the structure of the data by revealing the non-empty subspaces and how they are related. The graphical user interface allows the user to browse through the space of abstractions.
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© 1995 IFIP Series on Computer Graphics
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Oosthuizen, G.D., Venter, F.J. (1995). Using a Lattice for Visual Analysis of Categorical Data. In: Grinstein, G., Levkowitz, H. (eds) Perceptual Issues in Visualization. IFIP Series on Computer Graphics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-79057-7_12
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DOI: https://doi.org/10.1007/978-3-642-79057-7_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-79059-1
Online ISBN: 978-3-642-79057-7
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