  This paper develops projection pursuit for discrete data using the discrete
Radon transform. Discrete projection pursuit is presented as an exploratory
method for finding informative low dimensional views of data such as binary
vectors, rankings, phylogenetic trees or graphs. We show that for most data
sets, most projections are close to uniform. Thus, informative summaries are
ones deviating from uniformity. Syllabic data from several of Plato's great
works is used to illustrate the methods. Along with some basic distribution
theory, an automated procedure for computing informative projections is
introduced.
