Traditional Star Coordinates displays a multi-variate data set by mapping it to two Cartesian dimensions. This technique
facilitates cluster discovery and multi-variate analysis, but binding to two dimensions hides features of the data. Three-dimensional
Star Coordinates spreads out data elements to reveal features. This allows the user more intuitive freedom to
explore and process the data sets.
Three-dimensional Star Coordinates is implemented by extending the data structures and transformation facilities of
traditional Star Coordinates. We have given high priority to maintaining the simple, traditional interface. We simultaneously
extend existing features, such as scaling of axes, and add new features, such as system rotation in three dimensions.
These extensions and additions enhance data visualization and cluster discovery.
We use three examples to demonstrate the advantage of three-dimensional Star Coordinates over the traditional system.
First, in an analysis of customer churn data, system rotation in three dimensions gives the user new insight into the data.
Second, in cluster discovery of car data, the additional dimension allows the true shape of the data to be seen more easily.
Third, in a multi-variate analysis of cities, the perception of depth increases the degree to which multi-variate analysis can