batchSOM: Self-Organizing Maps: Batch Algorithm

In class: Functions for Classification

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

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.