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Visual clustering and classification: The Oronsay particle size data set revisited

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Summary

Interactive statistical graphics can be effectively used to find natural groupings in observations. In this paper we want to demonstrate how clustering and classification 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.

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Acknowledgments

We would like to thank Nick Fieller for providing us with the Oronsay particle size data set and additional background information. Thanks are also due to Walter Olbricht for his additional comments and to Qiang Luo who assisted with the preparation of data and the analysis. We are also grateful to Debby Swayne and the anonymous referees for their helpful comments and suggestions. The work of all three authors was supported in part by the NSF with a Group Infrastructure Grant DMS-9631351. In addition, the work of Edward Wegman was supported in part by the Army Research Office under grant DAAH04-94-G-0267. This work was initiated when Adalbert Wilhelm was visiting the Center for Computational Statistics as a Habilitanden-Stipendiat of the Deutsche Forschungsgemeinschaft under contract Wi 1584/1-1.

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Authors and Affiliations

  1. Institut für Mathematik, Universität Augsburg, D-86135, Augsburg, Germany

    Adalbert F. X. Wilhelm

  2. Center for Computational Statistics, George Mason University, Fairfax, VA, 22030, USA

    Edward J. Wegman & Jürgen Symanzik

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  1. Adalbert F. X. Wilhelm
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  2. Edward J. Wegman
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  3. Jürgen Symanzik
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Wilhelm, A.F.X., Wegman, E.J. & Symanzik, J. Visual clustering and classification: The Oronsay particle size data set revisited. Computational Statistics 14, 109–146 (1999). https://doi.org/10.1007/PL00022701

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