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The following are hints that can be used to find patterns in data in
each of the view methods.
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Scatterplots - look for linear features, which indicate a
pairwise relationship within the data. Clusters are a bit deceptive,
as there may be quite a spread amongst the other dimensions.
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Glyphs - look for similar shapes and simple transitions between
shapes, e.g. a convexity that grows or shrinks. It is also fairly
easy to spot anomalies, although if there are too many shapes the
anomalous ones may not stand out. One trick for examining dense data
with 2 spatial dimensions is to use square data sets (e.g. width =
height). Because the grid of glyphs uses the square root of the data
set size as the number of rows and columns, you in effect get 2
dimensions for free. Regions of similarity and boundaries between
distinct regions then manifest themselves as textures. The next
release of XmdvTool will allow using any 2 dimensions to position the
glyphs.
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Parallel Coordinates - relationships between adjacent
dimensions manifest themselves as line segments with similar
orientation. Adjacent dimensions with negative correlation create an
``X'' pattern between them.
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Dimensional Stacking - clusterings tend to appear as repeating
patterns in the inner-most dimensions. Transitions in the pattern
indicate clusters which are shifting along one or more dimension.
This method is most useful for fairly dense data sets, as then more of
the buckets get filled. Try adjusting the cardinality for dimensions
based on what you see - lower the cardinality of seemingly unimportant
or course dimensions, and increase it for important ones.