The grand tour and projection pursuit are two methods for exploring multivariate data. We show how to combine them into a dynamic graphical tool for exploratory data analysis, called a projection pursuit guided tour. This tool assists in clustering data when clusters are oddly shaped and in nding general low-dimensional structure in high dimensional, and in particular, sparse data. An example shows that the method, which is projection-based, can be quite powerful in situations which may cause methods based on kernel-smoothing grief. The projection pursuit guided tour is also useful for comparing and developing projection pursuit indices and illustrating some types of asymptotic results.
The early research on general plot-based data visualization is Grand Tour and Projection Pursuit REF .
Grand Tour and Projection Pursuit
Journal of Computational and Graphical Statistics
Computer Science