Visual Clustering and Classiication: the Oron- Say P Article Size Data Set Revisited

Interactive statistical graphics can be e ectivly used to nd natural groupings in observations. In this paper we want to demonstrate how clustering and classi cation can be done with three approaches based on highly interactive graphical environments: high{dimensional scatterplots as available in XGobi, parallel coordinate plots as available in ExplorN, and linked low{ dimensional views as available in Manet. We will point out the strengths and the weaknesses of these techniques by comparing their behavior when applied to the Oronsay particle size data set.