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

Daniel Fischer (), Alain Berro (), Klaus Nordhausen and Anne Ruiz-Gazen
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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 - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - UT2J - Université Toulouse - Jean Jaurès - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - CNRS - Centre National de la Recherche Scientifique - Toulouse INP - Institut National Polytechnique (Toulouse) - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - TMBI - Toulouse Mind & Brain Institut - UT2J - Université Toulouse - Jean Jaurès - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - UT3 - Université Toulouse III - Paul Sabatier - Comue de Toulouse - Communauté d'universités et établissements de Toulouse, UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse
Klaus Nordhausen: TU Wien - Vienna University of Technology = Technische Universität Wien

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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.

Keywords: Particle swarm optimization; Kurtosis; Genetic algorithms; Java; Projection index; Tribes; Projection matrix; Unsupervised data analysis (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-cmp
Note: View the original document on HAL open archive server: https://hal.science/hal-03548865v1
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Published in Communications in Statistics - Simulation and Computation, 2021, 50 (11), pp.3397-3419. ⟨10.1080/03610918.2019.1626880⟩

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Working Paper: REPPlab: An R package for detecting clusters and outliers using exploratory projection pursuit (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03548865

DOI: 10.1080/03610918.2019.1626880

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