Credible Visualization for Two-Dimensional Projections of Data

Projections are common dimensionality reduction methods, which represent high-dimensional data in a two-dimensional space. However, when restricting the output space to two dimensions, which results in a two dimensional scatter plot (projection) of the data, low dimensional similarities do not represent high dimensional distances coercively [Thrun, 2018] . This could lead to a misleading interpretation of the underlying structures [Thrun, 2018]. By means of the 3D topographic map the generalized Umatrix is able to depict errors of these two-dimensional scatter plots. The package is derived from the book of Thrun, M.C.: "Projection Based Clustering through Self-Organization and Swarm Intelligence" (2018) and the main algorithm called simplified self-organizing map for dimensionality reduction methods is published in .


Reference manual

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1.2.2 by Michael Thrun, a year ago

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Authors: Michael Thrun [aut, cre, cph] , Felix Pape [ctb, ctr] , Tim Schreier [ctb, ctr] , Luis Winckelman [ctb, ctr] , Alfred Ultsch [ths]

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, ggplot2

Suggests DataVisualizations, rgl, grid, mgcv, png, reshape2, fields, ABCanalysis, plotly, deldir, methods, knitr, rmarkdown

Linking to Rcpp, RcppArmadillo

System requirements: C++11, pandoc (>=1.12.3, needed for vignettes)

Imported by DatabionicSwarm, ProjectionBasedClustering.

Suggested by FCPS.

See at CRAN