SOMbrero ('Self Organizing Maps Bound to Realize Euclidean and Relational
Outputs') implements several variants of the stochastic Self-Organising Map
algorithm and is able to handle numeric and non numeric data sets (contingency
tables, graphs or any 'relational' data described by a dissimilarity matrix).
See help(SOMbrero) for further details.
The numeric SOM is illustrated on the well-known iris data set. This data
describe iris flowers with 4 numeric variables (Sepal.Length,
Sepal.Width, Petal.Length and Petal.Width) and a fifth
variable (not used to train the SOM) is the flower species. This example is
processed in the numeric SOM guide.
The SOM algorithm provided by the package SOMbrero can also handle some
non-numeric data. First, data described by contingency tables, which can be
processed using the 'korresp' algorithm (see Cottrell et al., 2004, 2005). This
case is illustrated on the presidentielles2002 dataset, which contains the
number of votes in the first round of the French 2002 presidential election, for
each of the French administrative departments (row variables) and each of the
candidates (column variables). This example is used in the
korresp user guide.
Data described by a dissimilarity matrix can also be processed by SOMbrero
as described in Olteanu et al., 2015a. This case is illustrated on a data set
extracted from the novel Les Miserables, written by the French author
Victor Hugo and published during the XIXth century. This dataset provides a
dissimilarity matrix between the characters of the novel, based on the length
of shortest paths in a network defined from the novel. This example is provided
in the relational user guide.
For those who have an R developer soul, and who want to help improve this package, the following picture provides an overview the current arborescence of the package:
This vignette has been computed with the following environment:
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.3 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=fr_FR.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=fr_FR.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=fr_FR.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] SOMbrero_1.4-1 markdown_1.1 igraph_1.2.10
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.7 pillar_1.6.2 compiler_4.1.2
## [4] tools_4.1.2 lubridate_1.7.10 checkmate_2.0.0
## [7] evaluate_0.14 lifecycle_1.0.0 tibble_3.1.3
## [10] gtable_0.3.0 pkgconfig_2.0.3 png_0.1-7
## [13] rlang_0.4.11 DBI_1.1.1 xfun_0.24
## [16] interp_1.0-33 metR_0.11.0 dplyr_1.0.7
## [19] stringr_1.4.0 knitr_1.33 generics_0.1.0
## [22] vctrs_0.3.8 scatterplot3d_0.3-41 grid_4.1.2
## [25] tidyselect_1.1.1 data.table_1.14.0 glue_1.4.2
## [28] R6_2.5.1 fansi_0.5.0 ggplot2_3.3.5
## [31] purrr_0.3.4 deldir_1.0-6 magrittr_2.0.1
## [34] backports_1.2.1 scales_1.1.1 ellipsis_0.3.2
## [37] ggwordcloud_0.5.0 assertthat_0.2.1 colorspace_2.0-2
## [40] utf8_1.2.2 stringi_1.6.2 munsell_0.5.0
## [43] crayon_1.4.1