batchSOM {class} | 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.

### See Also

`somgrid`, `SOM`

### Examples

    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))

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