Description Usage Arguments Details Value Note Author(s) References See Also Examples
The trainSOM
function returns a somRes
class
object which contains the outputs of the algorithm.
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x.data |
a data frame or matrix containing the observations to be mapped on the grid by the SOM algorithm. |
... |
Further arguments to be passed to the function
|
x |
an object of class |
object |
an object of class |
The version of the SOM algorithm implemented in this package is the stochastic version.
Several variants able to handle non-vectorial data are also implemented in
their stochastic versions: type="korresp"
for contingency tables, as
described in Cottrell et al. (2004) (with weights as in Cottrell and Letrémy,
2005a); type = "relational"
for dissimilarity matrices, as described
in Olteanu et al. (2015), with the fast implementation introduced in Mariette
et al. (2017).
Missing values are handled as described in Cottrell et al. (2005b), not using
missing entries of the selected observation during winner computation or
prototype updates. This allows to proceed with the imputation of missing
entries with the corresponding entries of the cluster prototype (with
impute
).
summary
produces a complete summary of the results that
displays the parameters of the SOM, quality criteria and ANOVA. For
type = "numeric"
the ANOVA is performed for each input variable and
test the difference of this variable across the clusters of the map. For
type = "relational"
a dissimilarity ANOVA is performed (Anderson,
2001), except that in the present version, a crude estimate of the p-value is
used which is based on the Fisher distribution and not on a permutation test.
The trainSOM
function returns an object of class somRes
which contains the following components:
clustering the final classification of the data.
prototypes the final coordinates of the prototypes.
energy the final energy of the map. For the numeric case, energy with data having missing entries is based on data imputation as described in Cottrell and Letrémy (2005b).
backup a list containing some intermediate backups of the
prototypes coordinates, clustering, energy and the indexes of the recorded
backups, if nb.save
is set to a value larger than 1.
data the original dataset used to train the algorithm.
parameters a list of the map's parameters, which is an object of
class paramSOM
as produced by the function initSOM
.
The function summary.somRes
also provides an ANOVA (ANalysis Of
VAriance) of each input numeric variables in function of the map's clusters.
This is helpful to see which variables participate to the clustering.
Warning! Recording intermediate backups with the argument
nb.save
can strongly increase the computational time since calculating
the entire clustering and the energy is time consuming. Use this option with
care and only when it is strictly necessary.
Élise Maigné <elise.maigne@inrae.fr>
Jérome Mariette jerome.mariette@inrae.fr
Madalina Olteanu olteanu@ceremade.dauphine.fr
Fabrice Rossi fabrice.rossi@apiacoa.org
Nathalie Vialaneix nathalie.vialaneix@inrae.fr
Anderson M.J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology, 26, 32-46.
Kohonen T. (2001) Self-Organizing Maps. Berlin/Heidelberg: Springer-Verlag, 3rd edition.
Cottrell M., Ibbou S., Letrémy P. (2004) SOM-based algorithms for qualitative variables. Neural Networks, 17, 1149-1167.
Cottrell M., Letrémy P. (2005a) How to use the Kohonen algorithm to simultaneously analyse individuals in a survey. Neurocomputing, 21, 119-138.
Cottrell M., Letrémy P. (2005b) Missing values: processing with the Kohonen algorithm. Proceedings of Applied Stochastic Models and Data Analysis (ASMDA 2005), 489-496.
Olteanu M., Villa-Vialaneix N. (2015) On-line relational and multiple relational SOM. Neurocomputing, 147, 15-30.
Mariette J., Rossi F., Olteanu M., Mariette J. (2017) Accelerating stochastic kernel SOM. In: M. Verleysen, XXVth European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2017), i6doc, Bruges, Belgium, 269-274.
See initSOM
for a description of the parameters to
pass to the trainSOM function to change its behavior and
plot.somRes
to plot the outputs of the algorithm.
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Loading required package: knitr
Loading required package: igraph
Attaching package: 'igraph'
The following objects are masked from 'package:stats':
decompose, spectrum
The following object is masked from 'package:base':
union
***********************************************************
This is 'SOMbrero' package, v 1.2
Citation details with citation('SOMbrero')
Further information with help(SOMbrero)...
Use sombreroGUI() to start the Graphical Interface.
Warning! This package has been implemented (mostly) by girls.
Default colors may not be suited for men.
***********************************************************
Self-Organizing Map object...
online learning, type: numeric
5 x 5 grid with square topology
neighbourhood type: gaussian
distance type: euclidean
Summary
Class : somRes
Self-Organizing Map object...
online learning, type: numeric
5 x 5 grid with square topology
neighbourhood type: gaussian
distance type: euclidean
Final energy : 0.8644138
Topographic error: 0
ANOVA :
Degrees of freedom : 15
F pvalue significativity
Sepal.Length 43.446 0 ***
Sepal.Width 16.801 0 ***
Petal.Length 307.412 0 ***
Petal.Width 177.034 0 ***
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