## Package description

`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.

Information on grids in
SOMbrero

### Numeric SOM

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.

### Contingency tables

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.

### Dissimilarity matrices

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 function dependencies of the package: