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 |

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

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

### 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 |

`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

### 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*version 7.3-22 Index]