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REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit

Author

Listed:
  • Daniel Fischer

    (LUKE - Natural Resources Institute Finland)

  • Alain Berro

    (IRIT-SEPIA - Système d’exploitation, systèmes répartis, de l’intergiciel à l’architecture - IRIT - Institut de recherche en informatique de Toulouse - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse - CNRS - Centre National de la Recherche Scientifique - Toulouse INP - Institut National Polytechnique (Toulouse) - UT - Université de Toulouse - TMBI - Toulouse Mind & Brain Institut - UT2J - Université Toulouse - Jean Jaurès - UT - Université de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - UT - Université de Toulouse, UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

  • Klaus Nordhausen

    (TU Wien - Vienna University of Technology = Technische Universität Wien)

  • Anne Ruiz-Gazen

    (TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse)

Abstract

The R-package REPPlab is designed to explore multivariate data sets using one-dimensional unsupervised projection pursuit. It is useful as a preprocessing step to find clusters or as an outlier detection tool for multivariate data. Except from the packages tourr and rggobi, there is no implementation of exploratory projection pursuit tools available in R. REPPlab is an R interface for the Java program EPP-lab that implements four projection indices and three biologically inspired optimization algorithms. It also proposes new tools for plotting and combining the results and specific tools for outlier detection. The functionality of the package is illustrated through some simulations and using some real data.

Suggested Citation

  • Daniel Fischer & Alain Berro & Klaus Nordhausen & Anne Ruiz-Gazen, 2021. "REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit," Post-Print hal-03548865, HAL.
    • Handle: RePEc:hal:journl:hal-03548865
    DOI: 10.1080/03610918.2019.1626880
    Note: View the original document on HAL open archive server: https://hal.science/hal-03548865
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    References listed on IDEAS

    as
    1. Wickham, Hadley & Cook, Dianne & Hofmann, Heike & Buja, Andreas, 2011. "tourr: An R Package for Exploring Multivariate Data with Projections," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i02).
    2. David E. Tyler & Frank Critchley & Lutz Dümbgen & Hannu Oja, 2009. "Invariant co‐ordinate selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 549-592, June.
    3. Huang, Bei & Cook, Dianne & Wickham, Hadley, 2012. "tourrGui: A gWidgets GUI for the Tour to Explore High-Dimensional Data Using Low-Dimensional Projections," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 49(i06).
    4. Nordhausen, Klaus & Oja, Hannu & Tyler, David E., 2008. "Tools for Exploring Multivariate Data: The Package ICS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i06).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Unsupervised data analysis; Projection matrix; Tribes; Projection index; Particle swarm optimization; Kurtosis; Java; Genetic algorithms;
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