Visualization Hints

The following are hints that can be used to find patterns in data in each of the view methods.

• 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.
• 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.
• 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.
• 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.