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

 batchSOM R Documentation

## 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`.

### 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

``````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\$codes, 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))
``````

class documentation built on May 3, 2023, 5:09 p.m.