# batchSOM: Self-Organizing Maps: Batch Algorithm In class: Functions for Classification

## Description

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

## Usage

 `1` ```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`.

## Details

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

## Value

An object of class `"SOM"` with components

 `grid` the grid, an object of class `"somgrid"`. `codes` a matrix of representatives.

## 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.

`somgrid`, `SOM`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```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)) ```

### Example output

```
```

class documentation built on Jan. 13, 2022, 9:07 a.m.