class (version 7.3-18)

# batchSOM: Self-Organizing Maps: Batch Algorithm

## Description

Kohonen's Self-Organizing Maps are a crude form of multidimensional scaling.

## Usage

`batchSOM(data, grid = somgrid(), radii, init)`

## Arguments

data

a matrix or data frame of observations, scaled so that Euclidean distance is appropriate.

grid

A grid for the representatives: see `somgrid`.

radii

the radii of the neighbourhood to be used for each pass: one pass is run for each element of `radii`.

init

the initial representatives. If missing, chosen (without replacement) randomly from `data`.

## Value

An object of class `"SOM"` with components

grid

the grid, an object of class `"somgrid"`.

codes

a matrix of representatives.

## Details

The batch SOM algorithm of Kohonen(1995, section 3.14) is used.

## References

Kohonen, T. (1995) Self-Organizing Maps. Springer-Verlag.

Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

## See Also

`somgrid`, `SOM`

## Examples

```# NOT RUN {
require(graphics)
data(crabs, package = "MASS")

lcrabs <- log(crabs[, 4:8])
crabs.grp <- factor(c("B", "b", "O", "o")[rep(1:4, rep(50,4))])
gr <- somgrid(topo = "hexagonal")
crabs.som <- batchSOM(lcrabs, gr, c(4, 4, 2, 2, 1, 1, 1, 0, 0))
plot(crabs.som)

bins <- as.numeric(knn1(crabs.som\$code, lcrabs, 0:47))
plot(crabs.som\$grid, type = "n")
symbols(crabs.som\$grid\$pts[, 1], crabs.som\$grid\$pts[, 2],
circles = rep(0.4, 48), inches = FALSE, add = TRUE)
text(crabs.som\$grid\$pts[bins, ] + rnorm(400, 0, 0.1),
as.character(crabs.grp))
# }
```